def analyse():
global global_vars
if threshold_vars["integration_time"] < 0.05:
threshold_vars["integration_time"] = 0.05
rep = Replay()
for i, ro in enumerate(tqdm(rep.get_data(r"/home/rasmus/Documents/Rasmus/110_mimosa_telescope_testbeam_14122016_fei4_self_trigger_scan.h5", real_time=True))):
raw_data = ro[0]
data_record = ru.convert_data_array(raw_data, filter_func=is_record)
# if np.any(is_trigger_word(data_record)):
# raise
#dr = is_data_record(raw_data)
global_vars["timestamp_start"].append(ro[1])
timestamp_stop = ro[2]
global_vars["hits"].append(len(data_record))
# print "{0:b}".format(ro[0][0]), FEI4Record(ro[0][0], chip_flavor="fei4b"), is_data_record(ro[0][0])
if np.any(data_record):
col, row = get_col_row_array_from_data_record_array(data_record)
global_vars["coloumn"].append(np.median(col))
global_vars["row"].append(np.median(row))
if not np.any(global_vars["hist_occ"]):
global_vars["hist_occ"] = fast_analysis_utils.hist_2d_index(col, row, shape=(81, 337))
# global_vars["hist_occ"] = fast_analysis_utils.hist_2d_index(np.mean(col), np.mean(row), shape=(81, 337))
else:
global_vars["hist_occ"] += fast_analysis_utils.hist_2d_index(col, row, shape=(81, 337))
# global_vars["hist_occ"] += fast_analysis_utils.hist_2d_index(np.mean(col), np.mean(row), shape=(81, 337))
if len(global_vars["time"]) == 0:
global_vars["time"].append(0)
if timestamp_stop - global_vars["timestamp_start"][0] > threshold_vars["integration_time"]:
global_vars["time"].append(global_vars["time"][-1] + timestamp_stop - global_vars["timestamp_start"][0])
global_vars["c"].append(np.var(global_vars["coloumn"]))
global_vars["r"].append(np.var(global_vars["row"]))
global_vars["hitrate"].append(np.sum(global_vars["hits"]) / (timestamp_stop - global_vars["timestamp_start"][0]))
if global_vars["analyse"]:
global_vars["beam"] = analyse_beam(global_vars["beam"])
p_hist = pickle.dumps(global_vars["hist_occ"], -1)
zlib_hist = zlib.compress(p_hist)
socket2.send(zlib_hist)
# # free memory of global variables
del global_vars["hits"][:]
del global_vars["timestamp_start"][:]
global_vars["hist_occ"] = None
if len(global_vars["coloumn"])>threshold_vars["reset_coloumn_row_arrays"]:
del global_vars["coloumn"][:]
del global_vars["row"][:]
def draw_hit_map_from_raw_data(raw_data_file, front_ends):
with PdfPages(os.path.splitext(raw_data_file)[0] + '.pdf') as output_pdf:
with tb.open_file(raw_data_file, 'r') as in_file_h5:
raw_data = in_file_h5.root.raw_data[:]
for front_end in range(front_ends):
print 'Create occupancy hist of front end %d' % front_end
occupancy_array, _, _ = np.histogram2d(*readout_utils.convert_data_array(raw_data,
filter_func=readout_utils.logical_and(readout_utils.is_data_record, readout_utils.is_data_from_channel(4 - front_end)),
converter_func=readout_utils.get_col_row_array_from_data_record_array), bins=(80, 336), range=[[1, 80], [1, 336]])
plotting.plot_three_way(hist=occupancy_array.T, title="Occupancy of chip %d" % front_end, x_axis_title="Occupancy", filename=output_pdf)
def draw_hit_map_from_raw_data(raw_data_file, front_ends):
with PdfPages(raw_data_file[:-3] + '.pdf') as output_pdf:
with tb.open_file(raw_data_file, 'r') as in_file_h5:
raw_data = in_file_h5.root.raw_data[:]
for front_end in range(front_ends):
print 'Create occupancy hist of front end %d' % front_end
occupancy_array, _, _ = np.histogram2d(*readout_utils.convert_data_array(raw_data,
filter_func=readout_utils.logical_and(readout_utils.is_data_record, readout_utils.is_data_from_channel(4 - front_end)),
converter_func=readout_utils.get_col_row_array_from_data_record_array), bins=(80, 336), range=[[1, 80], [1, 336]])
plotting.plotThreeWay(hist=occupancy_array.T, title="Occupancy of chip %d" % front_end, x_axis_title="Occupancy", filename=output_pdf)
def read_raw_data_from_fifo(self,
fifo,
filter_func=None,
converter_func=None):
'''Reads FIFO data and returns raw data array.
Returns
-------
data : np.array
An array containing FIFO data words.
'''
return convert_data_array(self.dut[fifo].get_data(),
filter_func=filter_func,
converter_func=converter_func)
def analyse(data_array):
global global_vars
if threshold_vars["integration_time"] < 0.05:
threshold_vars["integration_time"] = 0.05
for ro in data_array[0]:
raw_data = ro[0]
data_record = ru.convert_data_array(raw_data, filter_func=is_record)
global_vars["timestamp_start"].append(ro[1])
timestamp_stop = ro[2]
global_vars["hits"].append(len(data_record))
if np.any(data_record):
col, row = get_col_row_array_from_data_record_array(data_record)
global_vars["coloumn"].append(np.median(col))
global_vars["row"].append(np.median(row))
if not np.any(global_vars["hist_occ"]):
global_vars["hist_occ"] = fast_analysis_utils.hist_2d_index(
col, row, shape=(81, 337))
else:
global_vars["hist_occ"] += fast_analysis_utils.hist_2d_index(
col, row, shape=(81, 337))
if timestamp_stop - global_vars["timestamp_start"][0] > threshold_vars[
"integration_time"]:
global_vars["hitrate"].append(
np.sum(global_vars["hits"]) /
(timestamp_stop - global_vars["timestamp_start"][0]))
if (runmngr.current_run.run_id == "fei4_self_trigger_scan"
or runmngr.current_run.run_id
== "ext_trigger_scan") and global_vars["analyse"]:
global_vars["beam"] = analyse_beam(global_vars["beam"])
p_hist = pickle.dumps(global_vars["hist_occ"], -1)
zlib_hist = zlib.compress(p_hist)
socket2.send(zlib_hist)
# free memory of global variables
del global_vars["hits"][:]
del global_vars["timestamp_start"][:]
global_vars["hist_occ"] = None
#single variance gets diminished for long mesurements, this resets the variables after some time
#diminishing the number leads to more sensitivity
if len(global_vars["coloumn"]
) > threshold_vars["reset_coloumn_row_arrays"]:
del global_vars["coloumn"][:]
del global_vars["row"][:]
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 get_raw_data_from_buffer(self, filter_func=None, converter_func=None):
'''Reads local data buffer and returns raw data array.
Returns
-------
data : np.array
An array containing data words from the local data buffer.
'''
if self._is_running:
raise RuntimeError('Readout thread running')
if not self.fill_buffer:
logging.warning('Data buffer is not activated')
return [
convert_data_array(data_array_from_data_iterable(data_iterable),
filter_func=filter_func,
converter_func=converter_func)
for data_iterable in self._data_buffer
]
def scan(self):
if not self.plots_filename:
self.plots_filename = PdfPages(self.output_filename + '.pdf')
self.close_plots = True
else:
self.close_plots = False
mask_steps = 3
enable_mask_steps = [
0
] # one mask step to increase speed, no effect on precision
cal_lvl1_command = self.register.get_commands(
"CAL")[0] + self.register.get_commands(
"zeros", length=40)[0] + self.register.get_commands(
"LV1")[0] + self.register.get_commands(
"zeros", mask_steps=mask_steps)[0]
self.write_target_charge()
for feedback_bit in self.feedback_tune_bits: # reset all GDAC bits
self.set_prmp_vbpf_bit(feedback_bit, bit_value=0)
additional_scan = True
last_bit_result = self.n_injections_feedback
tot_mean_best = 0
feedback_best = self.register.get_global_register_value("PrmpVbpf")
for feedback_bit in self.feedback_tune_bits:
if additional_scan:
self.set_prmp_vbpf_bit(feedback_bit)
logging.info(
'PrmpVbpf setting: %d, bit %d = 1' %
(self.register.get_global_register_value("PrmpVbpf"),
feedback_bit))
else:
self.set_prmp_vbpf_bit(feedback_bit, bit_value=0)
logging.info(
'PrmpVbpf setting: %d, bit %d = 0' %
(self.register.get_global_register_value("PrmpVbpf"),
feedback_bit))
scan_parameter_value = self.register.get_global_register_value(
"PrmpVbpf")
with self.readout(PrmpVbpf=scan_parameter_value):
scan_loop(self,
cal_lvl1_command,
repeat_command=self.n_injections_feedback,
mask_steps=mask_steps,
enable_mask_steps=enable_mask_steps,
enable_double_columns=None,
same_mask_for_all_dc=True,
eol_function=None,
digital_injection=False,
enable_shift_masks=self.enable_shift_masks,
disable_shift_masks=self.disable_shift_masks,
restore_shift_masks=True,
mask=None,
double_column_correction=self.pulser_dac_correction)
self.raw_data_file.append(
self.fifo_readout.data,
scan_parameters=self.scan_parameters._asdict())
tots = convert_data_array(
data_array_from_data_iterable(self.fifo_readout.data),
filter_func=is_data_record,
converter_func=get_tot_array_from_data_record_array)
mean_tot = np.mean(tots)
if np.isnan(mean_tot):
logging.error(
"No hits, ToT calculation not possible, tuning will fail")
if abs(mean_tot - self.target_tot) < abs(tot_mean_best -
self.target_tot):
tot_mean_best = mean_tot
feedback_best = self.register.get_global_register_value(
"PrmpVbpf")
logging.info('Mean ToT = %f' % mean_tot)
self.tot_array, _ = np.histogram(a=tots, range=(0, 16), bins=16)
if self.plot_intermediate_steps:
plot_tot(hist=self.tot_array,
title='ToT distribution (PrmpVbpf ' +
str(scan_parameter_value) + ')',
filename=self.plots_filename)
if abs(
mean_tot - self.target_tot
) < self.max_delta_tot and feedback_bit > 0: # abort if good value already found to save time
logging.info(
'Good result already achieved, skipping missing bits')
break
if feedback_bit > 0 and mean_tot < self.target_tot:
self.set_prmp_vbpf_bit(feedback_bit, bit_value=0)
logging.info('Mean ToT = %f < %d ToT, set bit %d = 0' %
(mean_tot, self.target_tot, feedback_bit))
if feedback_bit == 0:
if additional_scan: # scan bit = 0 with the correct value again
additional_scan = False
last_bit_result = mean_tot
self.feedback_tune_bits.append(
0) # bit 0 has to be scanned twice
else:
logging.info(
'Scanned bit 0 = 0 with %f instead of %f for scanned bit 0 = 1'
% (mean_tot, last_bit_result))
if (abs(mean_tot - self.target_tot) >
abs(last_bit_result - self.target_tot)
): # if bit 0 = 0 is worse than bit 0 = 1, so go back
self.set_prmp_vbpf_bit(feedback_bit, bit_value=1)
mean_tot = last_bit_result
logging.info('Set bit 0 = 1')
else:
logging.info('Set bit 0 = 0')
if abs(mean_tot - self.target_tot) > abs(tot_mean_best -
self.target_tot):
logging.info(
"Binary search converged to non optimal value, take best measured value instead"
)
mean_tot = tot_mean_best
self.register.set_global_register_value(
"PrmpVbpf", feedback_best)
if self.register.get_global_register_value(
"PrmpVbpf") == 0 or self.register.get_global_register_value(
"PrmpVbpf") == 254:
logging.warning('PrmpVbpf reached minimum/maximum value')
if abs(mean_tot - self.target_tot) > 2 * self.max_delta_tot:
logging.warning(
'Global feedback tuning failed. Delta ToT = %f > %f. PrmpVbpf = %d'
% (abs(mean_tot - self.target_tot), self.max_delta_tot,
self.register.get_global_register_value("PrmpVbpf")))
else:
logging.info('Tuned PrmpVbpf to %d' %
self.register.get_global_register_value("PrmpVbpf"))
self.feedback_best = self.register.get_global_register_value(
"PrmpVbpf")
def scan(self):
def bits_set(int_type):
int_type = int(int_type)
position = 0
bits_set = []
while(int_type):
if(int_type & 1):
bits_set.append(position)
position += 1
int_type = int_type >> 1
return bits_set
# calculate selected pixels from the mask and the disabled columns
select_mask_array = np.zeros(shape=(80, 336), dtype=np.uint8)
if not self.enable_mask_steps_feedback:
self.enable_mask_steps_feedback = range(self.mask_steps)
for mask_step in self.enable_mask_steps_feedback:
select_mask_array += make_pixel_mask(steps=self.mask_steps, shift=mask_step)
for column in bits_set(self.register.get_global_register_value("DisableColumnCnfg")):
logging.info('Deselect double column %d' % column)
select_mask_array[column, :] = 0
cal_lvl1_command = self.register.get_commands("CAL")[0] + self.register.get_commands("zeros", length=40)[0] + self.register.get_commands("LV1")[0]
self.write_target_charge()
for feedback_bit in self.feedback_tune_bits: # reset all feedback bits
self.set_prmp_vbpf_bit(feedback_bit, bit_value=0)
additional_scan = True
tot_mean_best = 0.0
feedback_best = self.register.get_global_register_value("PrmpVbpf")
feedback_tune_bits = self.feedback_tune_bits[:]
for feedback_bit in feedback_tune_bits:
if self.stop_run.is_set():
break
if additional_scan:
self.set_prmp_vbpf_bit(feedback_bit, bit_value=1)
logging.info('PrmpVbpf setting: %d, set bit %d = 1', self.register.get_global_register_value("PrmpVbpf"), feedback_bit)
else:
self.set_prmp_vbpf_bit(feedback_bit, bit_value=0)
logging.info('PrmpVbpf setting: %d, set bit %d = 0', self.register.get_global_register_value("PrmpVbpf"), feedback_bit)
scan_parameter_value = self.register.get_global_register_value("PrmpVbpf")
with self.readout(PrmpVbpf=scan_parameter_value, fill_buffer=True):
scan_loop(self,
command=cal_lvl1_command,
repeat_command=self.n_injections_feedback,
mask_steps=self.mask_steps,
enable_mask_steps=self.enable_mask_steps_feedback,
enable_double_columns=None,
same_mask_for_all_dc=self.same_mask_for_all_dc,
eol_function=None,
digital_injection=False,
enable_shift_masks=self.enable_shift_masks,
disable_shift_masks=self.disable_shift_masks,
restore_shift_masks=True,
mask=None,
double_column_correction=self.pulser_dac_correction)
data = convert_data_array(array=self.read_data(), filter_func=is_data_record, converter_func=get_col_row_tot_array_from_data_record_array)
col_row_tot_array = np.column_stack(data)
occupancy_array, _, _ = np.histogram2d(col_row_tot_array[:, 0], col_row_tot_array[:, 1], bins=(80, 336), range=[[1, 80], [1, 336]])
occupancy_array = np.ma.array(occupancy_array, mask=np.logical_not(np.ma.make_mask(select_mask_array))) # take only selected pixel into account by creating a mask
occupancy_array = np.ma.masked_where(occupancy_array > self.n_injections_feedback, occupancy_array)
col_row_tot_hist = np.histogramdd(col_row_tot_array, bins=(80, 336, 16), range=[[1, 80], [1, 336], [0, 15]])[0]
tot_mean_array = np.average(col_row_tot_hist, axis=2, weights=range(0, 16)) * sum(range(0, 16)) / self.n_injections_feedback
tot_mean_array = np.ma.array(tot_mean_array, mask=occupancy_array.mask)
# keep noisy pixels out
mean_tot = np.ma.mean(tot_mean_array)
if abs(mean_tot - self.target_tot) < abs(tot_mean_best - self.target_tot):
tot_mean_best = mean_tot
feedback_best = self.register.get_global_register_value("PrmpVbpf")
logging.info('Mean ToT = %.2f', mean_tot)
tot_array = col_row_tot_array[:, 2]
self.tot_hist, _ = np.histogram(a=tot_array, range=(0, 16), bins=16)
if self.plot_intermediate_steps:
plot_tot(hist=self.tot_hist, title='ToT distribution (PrmpVbpf ' + str(scan_parameter_value) + ')', filename=self.plots_filename)
# if abs(mean_tot - self.target_tot) < self.max_delta_tot and feedback_bit > 0: # abort if good value already found to save time
# logging.info('Good result already achieved, skipping missing bits')
# break
if feedback_bit > 0:
# TODO: if feedback is to high, no hits
if mean_tot < self.target_tot:
self.set_prmp_vbpf_bit(feedback_bit, bit_value=0)
logging.info('Mean ToT = %.2f < %.2f ToT, set bit %d = 0', mean_tot, self.target_tot, feedback_bit)
else:
logging.info('Mean ToT = %.2f > %.2f ToT, keep bit %d = 1', mean_tot, self.target_tot, feedback_bit)
elif feedback_bit == 0:
if additional_scan: # scan bit = 0 with the correct value again
additional_scan = False
last_mean_tot = mean_tot
last_tot_hist = self.tot_hist.copy()
feedback_tune_bits.append(0) # bit 0 has to be scanned twice
else:
logging.info('Measured %.2f with bit 0 = 0 and %.2f with bit 0 = 1', mean_tot, last_mean_tot)
if(abs(mean_tot - self.target_tot) > abs(last_mean_tot - self.target_tot)): # if bit 0 = 0 is worse than bit 0 = 1, so go back
logging.info('Set bit 0 = 1')
self.set_prmp_vbpf_bit(0, bit_value=1)
self.tot_hist = last_tot_hist.copy()
mean_tot = last_mean_tot
else:
logging.info('Keep bit 0 = 0')
# select best Feedback value
if abs(mean_tot - self.target_tot) > abs(tot_mean_best - self.target_tot):
logging.info("Binary search converged to non-optimal value, apply best Feedback value, change PrmpVbpf from %d to %d", self.register.get_global_register_value("PrmpVbpf"), feedback_best)
mean_tot = tot_mean_best
self.register.set_global_register_value("PrmpVbpf", feedback_best)
self.feedback_best = self.register.get_global_register_value("PrmpVbpf")
if abs(mean_tot - self.target_tot) > 2 * self.max_delta_tot and not self.stop_run.is_set():
if np.all((((self.feedback_best & (1 << np.arange(self.register.global_registers['PrmpVbpf']['bitlength'])))) > 0).astype(int)[self.feedback_tune_bits] == 1):
if self.fail_on_warning:
raise RuntimeWarning('Selected Feedback bits reached maximum value')
else:
logging.warning('Selected Feedback bits reached maximum value')
elif np.all((((self.feedback_best & (1 << np.arange(self.register.global_registers['PrmpVbpf']['bitlength'])))) > 0).astype(int)[self.feedback_tune_bits] == 0):
if self.fail_on_warning:
raise RuntimeWarning('Selected Feedback bits reached minimum value')
else:
logging.warning('Selected Feedback bits reached minimum value')
else:
if self.fail_on_warning:
raise RuntimeWarning('Global feedback tuning failed. Delta ToT = %.2f > %.2f. PrmpVbpf = %d' % (abs(mean_tot - self.target_tot), self.max_delta_tot, self.register.get_global_register_value("PrmpVbpf")))
else:
logging.warning('Global feedback tuning failed. Delta ToT = %.2f > %.2f. PrmpVbpf = %d', abs(mean_tot - self.target_tot), self.max_delta_tot, self.register.get_global_register_value("PrmpVbpf"))
else:
logging.info('Tuned PrmpVbpf to %d', self.register.get_global_register_value("PrmpVbpf"))
def scan(self):
cal_lvl1_command = self.register.get_commands(
"CAL")[0] + self.register.get_commands(
"zeros", length=40)[0] + self.register.get_commands("LV1")[0]
self.write_target_threshold()
scan_parameter_range = [
(2**self.register.global_registers['Vthin_AltFine']['bitlength']),
0
] # high to low
if self.start_gdac:
scan_parameter_range[0] = self.start_gdac
if self.gdac_lower_limit:
scan_parameter_range[1] = self.gdac_lower_limit
scan_parameter_range = np.arange(scan_parameter_range[0],
scan_parameter_range[1] - 1,
self.step_size)
logging.info("Scanning %s from %d to %d", 'GDAC',
scan_parameter_range[0], scan_parameter_range[-1])
def bits_set(int_type):
int_type = int(int_type)
position = 0
bits_set = []
while (int_type):
if (int_type & 1):
bits_set.append(position)
position += 1
int_type = int_type >> 1
return bits_set
# calculate selected pixels from the mask and the disabled columns
select_mask_array = np.zeros(shape=(80, 336), dtype=np.uint8)
self.occ_array_sel_pixels_best = select_mask_array.copy()
self.occ_array_desel_pixels_best = select_mask_array.copy()
if not self.enable_mask_steps_gdac:
self.enable_mask_steps_gdac = range(self.mask_steps)
for mask_step in self.enable_mask_steps_gdac:
select_mask_array += make_pixel_mask(steps=self.mask_steps,
shift=mask_step)
for column in bits_set(
self.register.get_global_register_value("DisableColumnCnfg")):
logging.info('Deselect double column %d' % column)
select_mask_array[column, :] = 0
gdac_values = []
gdac_occupancies = []
gdac_occ_array_sel_pixels = []
gdac_occ_array_desel_pixels = []
median_occupancy_last_step = None
for scan_parameter_value in scan_parameter_range:
if self.stop_run.is_set():
break
self.register_utils.set_gdac(scan_parameter_value)
with self.readout(GDAC=scan_parameter_value, fill_buffer=True):
scan_loop(self,
command=cal_lvl1_command,
repeat_command=self.n_injections_gdac,
mask_steps=self.mask_steps,
enable_mask_steps=self.enable_mask_steps_gdac,
enable_double_columns=None,
same_mask_for_all_dc=self.same_mask_for_all_dc,
eol_function=None,
digital_injection=False,
enable_shift_masks=self.enable_shift_masks,
disable_shift_masks=self.disable_shift_masks,
restore_shift_masks=True,
mask=None,
double_column_correction=self.pulser_dac_correction)
data = convert_data_array(
array=self.read_data(),
filter_func=is_data_record,
converter_func=get_col_row_array_from_data_record_array)
occupancy_array, _, _ = np.histogram2d(*data,
bins=(80, 336),
range=[[1, 80], [1, 336]])
occ_array_sel_pixels = np.ma.array(
occupancy_array,
mask=np.logical_not(np.ma.make_mask(select_mask_array))
) # take only selected pixel into account by using the mask
occ_array_desel_pixels = np.ma.array(
occupancy_array, mask=np.ma.make_mask(select_mask_array)
) # take only de-selected pixel into account by using the inverted mask
median_occupancy = np.ma.median(occ_array_sel_pixels)
percentile_noise_occupancy = np.percentile(
occ_array_desel_pixels.compressed(), 99.0)
occupancy_almost_zero = np.allclose(median_occupancy, 0)
no_noise = np.allclose(percentile_noise_occupancy, 0)
gdac_values.append(self.register_utils.get_gdac())
gdac_occupancies.append(median_occupancy)
gdac_occ_array_sel_pixels.append(occ_array_sel_pixels.copy())
gdac_occ_array_desel_pixels.append(occ_array_desel_pixels.copy())
self.occ_array_sel_pixels_best = occ_array_sel_pixels.copy()
self.occ_array_desel_pixels_best = occ_array_desel_pixels.copy()
# abort early if threshold is found
if no_noise and not occupancy_almost_zero and (
median_occupancy_last_step is not None
and median_occupancy >= median_occupancy_last_step
) and median_occupancy >= self.n_injections_gdac / 2.0:
break
if no_noise and not occupancy_almost_zero:
median_occupancy_last_step = median_occupancy
else:
median_occupancy_last_step = 0.0
if not self.stop_run.is_set():
# select best GDAC value
sorted_indices = np.argsort(np.array(gdac_values))
occupancy_sorted = np.array(gdac_occupancies)[sorted_indices]
gdac_sorted = np.sort(gdac_values)
gdac_min_idx = np.where(
occupancy_sorted >= self.n_injections_gdac / 2.0)[0][-1]
occupancy_sorted_sel = occupancy_sorted[gdac_min_idx:]
gdac_sorted_sel = gdac_sorted[gdac_min_idx:]
best_index_sel = np.abs(
np.array(occupancy_sorted_sel) -
self.n_injections_gdac / 2.0).argmin()
best_index = sorted_indices[gdac_min_idx:][::-1][best_index_sel]
gdac_best = gdac_values[best_index]
median_occupancy = gdac_occupancies[best_index]
self.register_utils.set_gdac(gdac_best, send_command=False)
# for plotting
self.occ_array_sel_pixels_best = gdac_occ_array_sel_pixels[
best_index]
self.occ_array_desel_pixels_best = gdac_occ_array_desel_pixels[
best_index]
self.gdac_best = self.register_utils.get_gdac()
if abs(median_occupancy - self.n_injections_gdac / 2.0
) > self.max_delta_threshold and not self.stop_run.is_set():
if np.all((((self.gdac_best & (1 << np.arange(
self.register.global_registers['Vthin_AltFine']
['bitlength'] + self.register.
global_registers['Vthin_AltFine']['bitlength'])))) > 0
).astype(int) == 0):
if self.fail_on_warning:
raise RuntimeWarning(
'Selected GDAC bits reached minimum value')
else:
logging.warning(
'Selected GDAC bits reached minimum value')
else:
if self.fail_on_warning:
raise RuntimeWarning(
'Global threshold tuning failed. Delta threshold = %.2f > %.2f. Vthin_AltCoarse / Vthin_AltFine = %d / %d'
% (abs(median_occupancy - self.n_injections_gdac /
2.0), self.max_delta_threshold,
self.register.get_global_register_value(
"Vthin_AltCoarse"),
self.register.get_global_register_value(
"Vthin_AltFine")))
else:
logging.warning(
'Global threshold tuning failed. Delta threshold = %.2f > %.2f. Vthin_AltCoarse / Vthin_AltFine = %d / %d',
abs(median_occupancy -
self.n_injections_gdac / 2.0),
self.max_delta_threshold,
self.register.get_global_register_value(
"Vthin_AltCoarse"),
self.register.get_global_register_value(
"Vthin_AltFine"))
else:
logging.info(
'Tuned GDAC to Vthin_AltCoarse / Vthin_AltFine = %d / %d',
self.register.get_global_register_value("Vthin_AltCoarse"),
self.register.get_global_register_value("Vthin_AltFine"))
def scan(self):
enable_mask_steps = []
cal_lvl1_command = self.register.get_commands(
"CAL")[0] + self.register.get_commands(
"zeros", length=40)[0] + self.register.get_commands("LV1")[0]
self.write_target_charge()
additional_scan = True
last_tot_mean_array = np.zeros(
shape=self.register.get_pixel_register_value("FDAC").shape,
dtype=self.register.get_pixel_register_value("FDAC").dtype)
self.set_start_fdac()
self.tot_mean_best = np.full(
shape=(80, 336), fill_value=0
) # array to store the best occupancy (closest to Ninjections/2) of the pixel
self.fdac_mask_best = self.register.get_pixel_register_value("FDAC")
fdac_tune_bits = self.fdac_tune_bits[:]
for scan_parameter_value, fdac_bit in enumerate(fdac_tune_bits):
if self.stop_run.is_set():
break
if additional_scan:
self.set_fdac_bit(fdac_bit, bit_value=1)
logging.info('FDAC setting: bit %d = 1', fdac_bit)
else:
self.set_fdac_bit(fdac_bit, bit_value=0)
logging.info('FDAC setting: bit %d = 0', fdac_bit)
self.write_fdac_config()
with self.readout(FDAC=scan_parameter_value, fill_buffer=True):
scan_loop(self,
command=cal_lvl1_command,
repeat_command=self.n_injections_fdac,
mask_steps=self.mask_steps,
enable_mask_steps=enable_mask_steps,
enable_double_columns=None,
same_mask_for_all_dc=self.same_mask_for_all_dc,
eol_function=None,
digital_injection=False,
enable_shift_masks=self.enable_shift_masks,
disable_shift_masks=self.disable_shift_masks,
restore_shift_masks=True,
mask=None,
double_column_correction=self.pulser_dac_correction)
data = convert_data_array(
array=self.read_data(),
filter_func=is_data_record,
converter_func=get_col_row_tot_array_from_data_record_array)
col_row_tot = np.column_stack(data)
tot_array = np.histogramdd(col_row_tot,
bins=(80, 336, 16),
range=[[1, 80], [1, 336], [0, 15]])[0]
tot_mean_array = np.average(
tot_array, axis=2, weights=range(0, 16)) * sum(range(
0, 16)) / self.n_injections_fdac
select_better_pixel_mask = abs(
tot_mean_array - self.target_tot) <= abs(self.tot_mean_best -
self.target_tot)
self.tot_mean_best[select_better_pixel_mask] = tot_mean_array[
select_better_pixel_mask]
fdac_mask = self.register.get_pixel_register_value("FDAC")
self.fdac_mask_best[select_better_pixel_mask] = fdac_mask[
select_better_pixel_mask]
if fdac_bit > 0:
pixel_with_too_small_mean_tot_mask = tot_mean_array < self.target_tot
fdac_mask[pixel_with_too_small_mean_tot_mask] = fdac_mask[
pixel_with_too_small_mean_tot_mask] & ~(
1 << fdac_bit) # unset FDAC bit, increase ToT
self.register.set_pixel_register_value("FDAC", fdac_mask)
elif fdac_bit == 0:
if additional_scan: # scan bit = 0 with the correct value again
additional_scan = False
fdac_tune_bits.append(0) # bit 0 has to be scanned twice
else:
pass
if not self.stop_run.is_set():
self.register.set_pixel_register_value(
"FDAC", self.fdac_mask_best) # set value for meta scan
self.write_fdac_config()
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)
disabled_pixels_limit_cnt = int(self.disabled_pixels_limit * 336 * 80)
preselected_pixels = invert_pixel_mask(
self.register.get_pixel_register_value('Enable')).sum()
disabled_pixels = 0
for reg_val in range(scan_parameter_range[0],
scan_parameter_range[1] - 1, -1):
if self.stop_run.is_set():
break
self.register.create_restore_point(name=str(reg_val))
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
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):
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
self.raw_data_file.append(
self.fifo_readout.data,
scan_parameters=self.scan_parameters._asdict())
col_arr, row_arr = convert_data_array(
data_array_from_data_iterable(self.fifo_readout.data),
filter_func=is_data_record,
converter_func=get_col_row_array_from_data_record_array)
occ_hist, _, _ = np.histogram2d(col_arr,
row_arr,
bins=(80, 336),
range=[[1, 80], [1, 336]])
occ_mask = np.zeros(shape=occ_hist.shape,
dtype=np.dtype('>u1'))
# noisy pixels are set to 1
occ_mask[occ_hist > self.occupancy_limit * self.n_triggers *
self.consecutive_lvl1] = 1
# plot_occupancy(occ_hist.T, title='Occupancy', filename=self.scan_data_filename + '_noise_occ_' + str(reg_val) + '_' + str(step) + '.pdf')
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))
self.register.restore(name=str(reg_val))
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 # TODO
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.register.clear_restore_points(name=str(reg_val))
self.last_tdac_distribution = self.register.get_pixel_register_value(
'TDAC')
self.last_occupancy_hist = occ_hist.copy()
self.last_occupancy_mask = occ_mask.copy()
self.last_reg_val = reg_val
self.last_step = step
break
else:
logging.info(
'Found noisy pixels... repeat tuning step for Vthin_AltFine %d'
% (reg_val, ))
if disabled_pixels > disabled_pixels_limit_cnt:
self.last_good_threshold = self.register.get_global_register_value(
"Vthin_AltFine")
self.last_good_tdac = self.register.get_pixel_register_value(
'TDAC')
self.last_good_enable_mask = self.register.get_pixel_register_value(
'Enable')
break
def scan(self):
if not self.plots_filename:
self.plots_filename = PdfPages(self.output_filename + '.pdf')
self.close_plots = True
else:
self.close_plots = False
enable_mask_steps = []
cal_lvl1_command = self.register.get_commands(
"CAL")[0] + self.register.get_commands(
"zeros", length=40)[0] + self.register.get_commands("LV1")[0]
self.write_target_charge()
additional_scan = True
lastBitResult = np.zeros(
shape=self.register.get_pixel_register_value("FDAC").shape,
dtype=self.register.get_pixel_register_value("FDAC").dtype)
self.set_start_fdac()
self.tot_mean_best = np.full(
shape=(80, 336), fill_value=0
) # array to store the best occupancy (closest to Ninjections/2) of the pixel
self.fdac_mask_best = self.register.get_pixel_register_value("FDAC")
fdac_tune_bits = self.fdac_tune_bits[:]
for scan_parameter_value, fdac_bit in enumerate(fdac_tune_bits):
if additional_scan:
self.set_fdac_bit(fdac_bit)
logging.info('FDAC setting: bit %d = 1', fdac_bit)
else:
self.set_fdac_bit(fdac_bit, bit_value=0)
logging.info('FDAC setting: bit %d = 0', fdac_bit)
self.write_fdac_config()
with self.readout(FDAC=scan_parameter_value,
reset_sram_fifo=True,
fill_buffer=True,
clear_buffer=True,
callback=self.handle_data):
scan_loop(self,
command=cal_lvl1_command,
repeat_command=self.n_injections_fdac,
mask_steps=self.mask_steps,
enable_mask_steps=enable_mask_steps,
enable_double_columns=None,
same_mask_for_all_dc=self.same_mask_for_all_dc,
eol_function=None,
digital_injection=False,
enable_shift_masks=self.enable_shift_masks,
disable_shift_masks=self.disable_shift_masks,
restore_shift_masks=True,
mask=None,
double_column_correction=self.pulser_dac_correction)
col_row_tot = np.column_stack(
convert_data_array(
data_array_from_data_iterable(self.fifo_readout.data),
filter_func=logical_and(is_fe_word, is_data_record),
converter_func=get_col_row_tot_array_from_data_record_array
))
tot_array = np.histogramdd(col_row_tot,
bins=(80, 336, 16),
range=[[1, 80], [1, 336], [0, 15]])[0]
tot_mean_array = np.average(
tot_array, axis=2, weights=range(0, 16)) * sum(range(
0, 16)) / self.n_injections_fdac
select_better_pixel_mask = abs(
tot_mean_array - self.target_tot) <= abs(self.tot_mean_best -
self.target_tot)
pixel_with_too_small_mean_tot_mask = tot_mean_array < self.target_tot
self.tot_mean_best[select_better_pixel_mask] = tot_mean_array[
select_better_pixel_mask]
if self.plot_intermediate_steps:
plot_three_way(hist=tot_mean_array.transpose().transpose(),
title="Mean ToT (FDAC tuning bit " +
str(fdac_bit) + ")",
x_axis_title='mean ToT',
filename=self.plots_filename,
minimum=0,
maximum=15)
fdac_mask = self.register.get_pixel_register_value("FDAC")
self.fdac_mask_best[select_better_pixel_mask] = fdac_mask[
select_better_pixel_mask]
if fdac_bit > 0:
fdac_mask[pixel_with_too_small_mean_tot_mask] = fdac_mask[
pixel_with_too_small_mean_tot_mask] & ~(1 << fdac_bit)
self.register.set_pixel_register_value("FDAC", fdac_mask)
if fdac_bit == 0:
if additional_scan: # scan bit = 0 with the correct value again
additional_scan = False
lastBitResult = tot_mean_array.copy()
fdac_tune_bits.append(0) # bit 0 has to be scanned twice
else:
fdac_mask[abs(tot_mean_array - self.target_tot) > abs(
lastBitResult - self.target_tot
)] = fdac_mask[abs(tot_mean_array - self.target_tot) > abs(
lastBitResult - self.target_tot)] | (1 << fdac_bit)
tot_mean_array[abs(tot_mean_array - self.target_tot) > abs(
lastBitResult - self.target_tot)] = lastBitResult[
abs(tot_mean_array -
self.target_tot) > abs(lastBitResult -
self.target_tot)]
self.tot_mean_best[
abs(tot_mean_array - self.target_tot) <= abs(
self.tot_mean_best -
self.n_injections_fdac / 2)] = tot_mean_array[
abs(tot_mean_array - self.target_tot) <= abs(
self.tot_mean_best -
self.n_injections_fdac / 2)]
self.fdac_mask_best[
abs(tot_mean_array - self.target_tot) <= abs(
self.tot_mean_best -
self.n_injections_fdac / 2)] = fdac_mask[
abs(tot_mean_array - self.target_tot) <= abs(
self.tot_mean_best -
self.n_injections_fdac / 2)]
self.register.set_pixel_register_value(
"FDAC", self.fdac_mask_best) # set value for meta scan
self.write_fdac_config()
def scan(self):
enable_mask_steps = []
cal_lvl1_command = self.register.get_commands(
"CAL")[0] + self.register.get_commands(
"zeros", length=40)[0] + self.register.get_commands("LV1")[0]
self.write_target_threshold()
additional_scan = True
lastBitResult = np.zeros(
shape=self.register.get_pixel_register_value("TDAC").shape,
dtype=self.register.get_pixel_register_value("TDAC").dtype)
self.set_start_tdac()
self.occupancy_best = np.full(
shape=(80, 336), fill_value=self.n_injections_tdac
) # array to store the best occupancy (closest to Ninjections/2) of the pixel
self.tdac_mask_best = self.register.get_pixel_register_value("TDAC")
tdac_tune_bits = self.tdac_tune_bits[:]
for scan_parameter_value, tdac_bit in enumerate(tdac_tune_bits):
if self.stop_run.is_set():
break
if additional_scan:
self.set_tdac_bit(tdac_bit)
logging.info('TDAC setting: bit %d = 1', tdac_bit)
else:
self.set_tdac_bit(tdac_bit, bit_value=0)
logging.info('TDAC setting: bit %d = 0', tdac_bit)
self.write_tdac_config()
with self.readout(TDAC=scan_parameter_value, fill_buffer=True):
scan_loop(self,
command=cal_lvl1_command,
repeat_command=self.n_injections_tdac,
mask_steps=self.mask_steps,
enable_mask_steps=enable_mask_steps,
enable_double_columns=None,
same_mask_for_all_dc=self.same_mask_for_all_dc,
eol_function=None,
digital_injection=False,
enable_shift_masks=self.enable_shift_masks,
disable_shift_masks=self.disable_shift_masks,
restore_shift_masks=True,
mask=None,
double_column_correction=self.pulser_dac_correction)
data = convert_data_array(
array=self.read_data(),
filter_func=is_data_record,
converter_func=get_col_row_array_from_data_record_array)
occupancy_array, _, _ = np.histogram2d(*data,
bins=(80, 336),
range=[[1, 80], [1, 336]])
select_better_pixel_mask = abs(occupancy_array -
self.n_injections_tdac / 2) <= abs(
self.occupancy_best -
self.n_injections_tdac / 2)
pixel_with_too_high_occupancy_mask = occupancy_array > self.n_injections_tdac / 2
self.occupancy_best[select_better_pixel_mask] = occupancy_array[
select_better_pixel_mask]
if self.plot_intermediate_steps:
plot_three_way(occupancy_array.transpose(),
title="Occupancy (TDAC tuning bit " +
str(tdac_bit) + ")",
x_axis_title='Occupancy',
filename=self.plots_filename,
maximum=self.n_injections_tdac)
tdac_mask = self.register.get_pixel_register_value("TDAC")
self.tdac_mask_best[select_better_pixel_mask] = tdac_mask[
select_better_pixel_mask]
if tdac_bit > 0:
tdac_mask[pixel_with_too_high_occupancy_mask] = tdac_mask[
pixel_with_too_high_occupancy_mask] & ~(1 << tdac_bit)
self.register.set_pixel_register_value("TDAC", tdac_mask)
if tdac_bit == 0:
if additional_scan: # scan bit = 0 with the correct value again
additional_scan = False
lastBitResult = occupancy_array.copy()
tdac_tune_bits.append(0) # bit 0 has to be scanned twice
else:
tdac_mask[
abs(occupancy_array - self.n_injections_tdac / 2) >
abs(lastBitResult -
self.n_injections_tdac / 2)] = tdac_mask[
abs(occupancy_array - self.n_injections_tdac /
2) > abs(lastBitResult -
self.n_injections_tdac / 2)] | (
1 << tdac_bit)
occupancy_array[
abs(occupancy_array - self.n_injections_tdac / 2) >
abs(lastBitResult -
self.n_injections_tdac / 2)] = lastBitResult[
abs(occupancy_array - self.n_injections_tdac /
2) > abs(lastBitResult -
self.n_injections_tdac / 2)]
self.occupancy_best[
abs(occupancy_array - self.n_injections_tdac / 2) <=
abs(self.occupancy_best -
self.n_injections_tdac / 2)] = occupancy_array[
abs(occupancy_array - self.n_injections_tdac /
2) <= abs(self.occupancy_best -
self.n_injections_tdac / 2)]
self.tdac_mask_best[
abs(occupancy_array - self.n_injections_tdac / 2) <=
abs(self.occupancy_best -
self.n_injections_tdac / 2)] = tdac_mask[
abs(occupancy_array - self.n_injections_tdac /
2) <= abs(self.occupancy_best -
self.n_injections_tdac / 2)]
self.register.set_pixel_register_value(
"TDAC", self.tdac_mask_best) # set value for meta scan
self.write_tdac_config()
def scan(self):
if not self.plots_filename:
self.plots_filename = PdfPages(self.output_filename + '.pdf')
self.close_plots = True
else:
self.close_plots = False
enable_mask_steps = []
cal_lvl1_command = self.register.get_commands("CAL")[0] + self.register.get_commands("zeros", length=40)[0] + self.register.get_commands("LV1")[0] + self.register.get_commands("zeros", mask_steps=self.mask_steps)[0]
self.write_target_charge()
additional_scan = True
lastBitResult = np.zeros(shape=self.register.get_pixel_register_value("FDAC").shape, dtype=self.register.get_pixel_register_value("FDAC").dtype)
self.set_start_fdac()
self.tot_mean_best = np.empty(shape=(80, 336)) # array to store the best occupancy (closest to Ninjections/2) of the pixel
self.tot_mean_best.fill(0)
self.fdac_mask_best = self.register.get_pixel_register_value("FDAC")
for scan_parameter_value, fdac_bit in enumerate(self.fdac_tune_bits):
if additional_scan:
self.set_fdac_bit(fdac_bit)
logging.info('FDAC setting: bit %d = 1', fdac_bit)
else:
self.set_fdac_bit(fdac_bit, bit_value=0)
logging.info('FDAC setting: bit %d = 0', fdac_bit)
self.write_fdac_config()
with self.readout(FDAC=scan_parameter_value, reset_sram_fifo=True, fill_buffer=True, clear_buffer=True, callback=self.handle_data):
scan_loop(self, cal_lvl1_command, repeat_command=self.n_injections_fdac, mask_steps=self.mask_steps, enable_mask_steps=enable_mask_steps, enable_double_columns=None, same_mask_for_all_dc=True, eol_function=None, digital_injection=False, enable_shift_masks=self.enable_shift_masks, disable_shift_masks=self.disable_shift_masks, restore_shift_masks=True, mask=None, double_column_correction=self.pulser_dac_correction)
col_row_tot = np.column_stack(convert_data_array(data_array_from_data_iterable(self.fifo_readout.data), filter_func=is_data_record, converter_func=get_col_row_tot_array_from_data_record_array))
tot_array = np.histogramdd(col_row_tot, bins=(80, 336, 16), range=[[1, 80], [1, 336], [0, 15]])[0]
tot_mean_array = np.average(tot_array, axis=2, weights=range(0, 16)) * sum(range(0, 16)) / self.n_injections_fdac
select_better_pixel_mask = abs(tot_mean_array - self.target_tot) <= abs(self.tot_mean_best - self.target_tot)
pixel_with_too_small_mean_tot_mask = tot_mean_array < self.target_tot
self.tot_mean_best[select_better_pixel_mask] = tot_mean_array[select_better_pixel_mask]
if self.plot_intermediate_steps:
plot_three_way(hist=tot_mean_array.transpose().transpose(), title="Mean ToT (FDAC tuning bit " + str(fdac_bit) + ")", x_axis_title='mean ToT', filename=self.plots_filename, minimum=0, maximum=15)
fdac_mask = self.register.get_pixel_register_value("FDAC")
self.fdac_mask_best[select_better_pixel_mask] = fdac_mask[select_better_pixel_mask]
if fdac_bit > 0:
fdac_mask[pixel_with_too_small_mean_tot_mask] = fdac_mask[pixel_with_too_small_mean_tot_mask] & ~(1 << fdac_bit)
self.register.set_pixel_register_value("FDAC", fdac_mask)
if fdac_bit == 0:
if additional_scan: # scan bit = 0 with the correct value again
additional_scan = False
lastBitResult = tot_mean_array.copy()
self.fdac_tune_bits.append(0) # bit 0 has to be scanned twice
else:
fdac_mask[abs(tot_mean_array - self.target_tot) > abs(lastBitResult - self.target_tot)] = fdac_mask[abs(tot_mean_array - self.target_tot) > abs(lastBitResult - self.target_tot)] | (1 << fdac_bit)
tot_mean_array[abs(tot_mean_array - self.target_tot) > abs(lastBitResult - self.target_tot)] = lastBitResult[abs(tot_mean_array - self.target_tot) > abs(lastBitResult - self.target_tot)]
self.tot_mean_best[abs(tot_mean_array - self.target_tot) <= abs(self.tot_mean_best - self.n_injections_fdac / 2)] = tot_mean_array[abs(tot_mean_array - self.target_tot) <= abs(self.tot_mean_best - self.n_injections_fdac / 2)]
self.fdac_mask_best[abs(tot_mean_array - self.target_tot) <= abs(self.tot_mean_best - self.n_injections_fdac / 2)] = fdac_mask[abs(tot_mean_array - self.target_tot) <= abs(self.tot_mean_best - self.n_injections_fdac / 2)]
self.register.set_pixel_register_value("FDAC", self.fdac_mask_best) # set value for meta scan
self.write_fdac_config()
def scan(self):
if self.trig_count == 0:
self.consecutive_lvl1 = 2 ** self.register.global_registers['Trig_Count']['bitlength']
else:
self.consecutive_lvl1 = self.trig_count
enabled_pixels = self.register.get_pixel_register_value('Enable').sum()
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())
abs_occ_limit = stats.poisson.ppf(0.5, mu=self.occupancy_limit * self.n_triggers * self.consecutive_lvl1)
logging.info('The pixel threshold will be increased when occpancy >%d' % abs_occ_limit)
total_occ_limit = int(self.occupancy_limit * self.n_triggers * self.consecutive_lvl1 * enabled_pixels)
# Sum of PMF of Poisson distribution (k>0)
n_expected_pixel_hits = int((1.0 - stats.poisson.pmf(k=0, mu=self.occupancy_limit * self.n_triggers * self.consecutive_lvl1)) * enabled_pixels)
logging.info('The global threshold will be decreased when total occupancy is <=%d and pixel with hits <=%d' % (total_occ_limit, n_expected_pixel_hits))
max_tdac_steps = max(1, int(np.ceil((1 / self.occupancy_limit) / (self.n_triggers * self.consecutive_lvl1) * (1 / (1 - 0.5)))))
tdac_center = 2 ** self.register.pixel_registers['TDAC']['bitlength'] / 2
lvl1_command = self.register.get_commands("LV1")[0] + self.register.get_commands("zeros", length=self.trigger_rate_limit)[0]
total_scan_time = int(lvl1_command.length() * 25 * (10 ** -9) * self.n_triggers)
self.threshold = deque([None] * (self.plot_n_steps + 2), maxlen=self.plot_n_steps + 2)
self.tdac_step = deque([None] * (self.plot_n_steps + 2), maxlen=self.plot_n_steps + 2)
self.tdac = deque([None] * (self.plot_n_steps + 2), maxlen=self.plot_n_steps + 2)
self.new_tdac = deque([None] * (self.plot_n_steps + 2), maxlen=self.plot_n_steps + 2)
self.enable_mask = deque([None] * (self.plot_n_steps + 2), maxlen=self.plot_n_steps + 2)
self.new_enable_mask = deque([None] * (self.plot_n_steps + 2), maxlen=self.plot_n_steps + 2)
self.occupancy_hist = deque([None] * (self.plot_n_steps + 2), maxlen=self.plot_n_steps + 2)
self.occupancy_mask = deque([None] * (self.plot_n_steps + 2), maxlen=self.plot_n_steps + 2)
# interpreter = PyDataInterpreter()
# histogram = PyDataHistograming()
# interpreter.set_trig_count(self.trig_count)
# interpreter.set_warning_output(False)
# histogram.set_no_scan_parameter()
# histogram.create_occupancy_hist(True)
coarse_threshold = [self.gdac_range[0]]
reached_pixels_limit_cnt = False
reached_tdac_center = False
reached_gdac_lower_limit = False
do_refine_tuning = False
for reg_val in coarse_threshold: # outer loop, coarse tuning threshold
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)
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)
tdac_step = 1
while True: # inner loop
if self.stop_run.is_set():
break
# histogram.reset()
logging.info('TDAC step %d at Vthin_AltFine %d', tdac_step, reg_val)
# logging.info('Estimated scan time: %ds', total_scan_time)
with self.readout(Vthin_AltFine=reg_val, TDAC_step=tdac_step, fill_buffer=True):
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.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=total_scan_time, poll=10, term_width=80).start()
else:
try:
self.progressbar.update(time() - start)
except ValueError:
pass
# use Numpy for analysis and histogramming
col_arr, row_arr = convert_data_array(array=self.read_data(), filter_func=is_data_record, converter_func=get_col_row_array_from_data_record_array)
occ_hist, _, _ = np.histogram2d(col_arr, row_arr, bins=(80, 336), range=[[1, 80], [1, 336]])
occ_mask = np.zeros(shape=occ_hist.shape, dtype=np.dtype('>u1'))
# use FEI4 interpreter for analysis and histogramming
# from pybar.daq.readout_utils import data_array_from_data_iterable
# raw_data = np.ascontiguousarray(data_array_from_data_iterable(self.read_data()), dtype=np.uint32)
# interpreter.interpret_raw_data(raw_data)
# interpreter.store_event() # force to create latest event
# histogram.add_hits(interpreter.get_hits())
# occ_hist = 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 > 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_mask = self.register.get_pixel_register_value('Enable')
new_enable_mask = np.logical_and(enable_mask, invert_pixel_mask(disable_pixel_mask))
if np.logical_and(occ_mask > 0, enable_mask == 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(new_enable_mask).sum() - preselected_pixels
logging.info('Found %d noisy pixels', occ_mask.sum())
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)
# increasing threshold before writing TDACs to avoid FE becoming noisy
self.register.set_global_register_value("Vthin_AltFine", self.gdac_range[0])
commands = []
commands.extend(self.register.get_commands("ConfMode"))
commands.extend(self.register.get_commands("WrRegister", name=["Vthin_AltFine"]))
self.register_utils.send_commands(commands)
# writing TDAC
new_tdac_reg = tdac_reg.copy()
new_tdac_reg[decrease_pixel_mask] -= 1 # smaller TDAC translates to higher threshold
self.register.set_pixel_register_value('TDAC', new_tdac_reg)
self.register.set_pixel_register_value('Enable', new_enable_mask)
commands = []
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)
# writing threshold value after writing TDACs
self.register.set_global_register_value("Vthin_AltFine", reg_val)
commands = []
commands.extend(self.register.get_commands("WrRegister", name=["Vthin_AltFine"]))
commands.extend(self.register.get_commands("RunMode"))
self.register_utils.send_commands(commands)
if (not do_refine_tuning and occ_hist.sum() <= total_occ_limit and occ_mask.sum() <= n_expected_pixel_hits) or (tdac_step >= max_tdac_steps):
logging.info('Stop tuning TDACs at Vthin_AltFine %d', reg_val)
self.threshold.appendleft(self.register.get_global_register_value("Vthin_AltFine"))
self.tdac_step.appendleft(tdac_step)
self.tdac.appendleft(tdac_reg)
self.new_tdac.appendleft(new_tdac_reg)
self.enable_mask.appendleft(enable_mask)
self.new_enable_mask.appendleft(new_enable_mask)
self.occupancy_hist.appendleft(occ_hist.copy())
self.occupancy_mask.appendleft(occ_mask.copy())
if not do_refine_tuning and np.mean(new_tdac_reg[new_enable_mask]) <= tdac_center + (1 if self.refine_tuning else 0):
reached_tdac_center = True
if not do_refine_tuning and disabled_pixels > disabled_pixels_limit_cnt:
reached_pixels_limit_cnt = True
logging.info('Limit of disabled pixels reached: %d (limit %d).' % (disabled_pixels, disabled_pixels_limit_cnt))
if not do_refine_tuning and reg_val <= self.gdac_range[1]:
reached_gdac_lower_limit = True
break
else:
logging.info('Continue tuning TDACs at Vthin_AltFine %d', reg_val)
# increase scan parameter counter
tdac_step += 1
if not self.refine_tuning and (reached_pixels_limit_cnt or reached_tdac_center or reached_gdac_lower_limit):
pass # will exit loop
elif do_refine_tuning:
logging.info("Finished TDAC refine tuning.")
elif reached_pixels_limit_cnt or reached_tdac_center or reached_gdac_lower_limit:
do_refine_tuning = True
logging.info("Starting TDAC refine tuning...")
coarse_threshold.append(reg_val - 1)
abs_occ_limit = stats.poisson.ppf(0.99, mu=self.occupancy_limit * self.n_triggers * self.consecutive_lvl1)
logging.info('The pixel threshold will be increased when occpancy >%d' % abs_occ_limit)
max_tdac_steps = max(1, int(np.ceil((1 / self.occupancy_limit) / (self.n_triggers * self.consecutive_lvl1) * (1 / (1 - 0.99)))))
else:
coarse_threshold.append(reg_val - 1)
def scan(self):
cal_lvl1_command = self.register.get_commands(
"CAL")[0] + self.register.get_commands(
"zeros", length=40)[0] + self.register.get_commands("LV1")[0]
self.write_target_threshold()
def bits_set(int_type):
int_type = int(int_type)
position = 0
bits_set = []
while (int_type):
if (int_type & 1):
bits_set.append(position)
position += 1
int_type = int_type >> 1
return bits_set
# calculate selected pixels from the mask and the disabled columns
select_mask_array = np.zeros(shape=(80, 336), dtype=np.uint8)
if not self.enable_mask_steps_gdac:
self.enable_mask_steps_gdac = range(self.mask_steps)
for mask_step in self.enable_mask_steps_gdac:
select_mask_array += make_pixel_mask(steps=self.mask_steps,
shift=mask_step)
for column in bits_set(
self.register.get_global_register_value("DisableColumnCnfg")):
logging.info('Deselect double column %d' % column)
select_mask_array[column, :] = 0
gdacs_above_threshold = []
additional_scan_ongoing = False
last_good_gdac_bit = self.gdac_tune_bits[0]
last_good_gdac_scan_step = 0
gdac_tune_bits_permutation = 0
gdac_values = []
gdac_occupancies = []
gdac_occ_array_sel_pixels = []
gdac_occ_array_desel_pixels = []
gdac_tune_bits = self.gdac_tune_bits[:]
min_gdac_with_occupancy = None
for gdac_scan_step, gdac_bit in enumerate(gdac_tune_bits):
if self.stop_run.is_set():
break
# set all higher GDAC bits
gdac_tune_bits_permutation_header = map(
int,
bin(2**last_good_gdac_scan_step - 1 -
gdac_tune_bits_permutation)[2:].zfill(
last_good_gdac_scan_step))[-last_good_gdac_scan_step:]
for gdac_permutation_bit, gdac_permutation_bit_value in enumerate(
gdac_tune_bits_permutation_header):
self.set_gdac_bit(self.gdac_tune_bits[gdac_permutation_bit],
bit_value=gdac_permutation_bit_value,
send_command=False)
# clear all lower GDAC bits
for clear_gdac_bit in self.gdac_tune_bits:
if clear_gdac_bit < gdac_bit:
self.set_gdac_bit(clear_gdac_bit,
bit_value=0,
send_command=False)
if additional_scan_ongoing:
self.set_gdac_bit(gdac_bit, bit_value=0, send_command=True)
scan_parameter_value = (
self.register.get_global_register_value("Vthin_AltCoarse")
<< 8
) + self.register.get_global_register_value("Vthin_AltFine")
logging.info('GDAC setting: %d, set bit %d = 0',
scan_parameter_value, gdac_bit)
else: # default
self.set_gdac_bit(gdac_bit, bit_value=1, send_command=False)
scan_parameter_value = (
self.register.get_global_register_value("Vthin_AltCoarse")
<< 8
) + self.register.get_global_register_value("Vthin_AltFine")
logging.info('GDAC setting: %d, set bit %d = 1',
scan_parameter_value, gdac_bit)
# check if GDAC values are too low or were already scanned
if not additional_scan_ongoing and (
(self.register_utils.get_gdac() in gdac_values) or
(self.gdac_lower_limit
and self.register_utils.get_gdac() < self.gdac_lower_limit) or
(min_gdac_with_occupancy and
self.register_utils.get_gdac() <= min_gdac_with_occupancy)):
if gdac_tune_bits_permutation + 1 == 2**last_good_gdac_scan_step: # next permutation step
gdac_tune_bits_permutation = 0
last_good_gdac_scan_step += 1
else:
gdac_tune_bits_permutation += 1
for i in range(len(gdac_tune_bits) - (gdac_scan_step + 1)):
gdac_tune_bits.pop()
gdac_tune_bits.extend(
self.gdac_tune_bits[last_good_gdac_scan_step:]
) # repeat all scan steps from last bit
continue
# write GDAC
self.register_utils.set_gdac(self.register_utils.get_gdac(),
send_command=True)
# start scan loop
with self.readout(GDAC=scan_parameter_value, fill_buffer=True):
scan_loop(self,
command=cal_lvl1_command,
repeat_command=self.n_injections_gdac,
mask_steps=self.mask_steps,
enable_mask_steps=self.enable_mask_steps_gdac,
enable_double_columns=None,
same_mask_for_all_dc=self.same_mask_for_all_dc,
eol_function=None,
digital_injection=False,
enable_shift_masks=self.enable_shift_masks,
disable_shift_masks=self.disable_shift_masks,
restore_shift_masks=True,
mask=None,
double_column_correction=self.pulser_dac_correction)
# calculate arrays from data
data = convert_data_array(
array=self.read_data(),
filter_func=is_data_record,
converter_func=get_col_row_array_from_data_record_array)
occupancy_array, _, _ = np.histogram2d(*data,
bins=(80, 336),
range=[[1, 80], [1, 336]])
occ_array_sel_pixels = np.ma.array(
occupancy_array,
mask=np.logical_not(np.ma.make_mask(select_mask_array))
) # take only selected pixel into account by using the mask
occ_array_desel_pixels = np.ma.array(
occupancy_array, mask=np.ma.make_mask(select_mask_array)
) # take only de-selected pixel into account by using the inverted mask
median_occupancy = np.ma.median(occ_array_sel_pixels)
percentile_noise_occupancy = np.percentile(
occ_array_desel_pixels.compressed(), 99.0)
occupancy_almost_zero = np.allclose(median_occupancy, 0)
no_noise = np.allclose(percentile_noise_occupancy, 0)
gdac_values.append(self.register_utils.get_gdac())
gdac_occupancies.append(median_occupancy)
gdac_occ_array_sel_pixels.append(occ_array_sel_pixels.copy())
gdac_occ_array_desel_pixels.append(occ_array_desel_pixels.copy())
self.occ_array_sel_pixels_best = occ_array_sel_pixels.copy()
self.occ_array_desel_pixels_best = occ_array_desel_pixels.copy()
if len(gdac_values) >= 2:
for index, scanned_gdac in enumerate(gdac_values[:-1]):
if (self.register_utils.get_gdac() < scanned_gdac
and median_occupancy <= gdac_occupancies[index]
and gdac_occupancies[index] != 0):
if min_gdac_with_occupancy is None:
min_gdac_with_occupancy = self.register_utils.get_gdac(
)
else:
min_gdac_with_occupancy = max(
min_gdac_with_occupancy,
self.register_utils.get_gdac())
if (scanned_gdac < self.register_utils.get_gdac()
and gdac_occupancies[index] <= median_occupancy
and median_occupancy != 0):
if min_gdac_with_occupancy is None:
min_gdac_with_occupancy = scanned_gdac
else:
min_gdac_with_occupancy = max(
min_gdac_with_occupancy, scanned_gdac)
for gdac_above_threshold in gdacs_above_threshold:
if gdac_above_threshold <= min_gdac_with_occupancy: # check for valid values
gdacs_above_threshold.remove(gdac_above_threshold)
if gdac_scan_step + 1 == len(
gdac_tune_bits): # last GDAC scan step
if not additional_scan_ongoing and (
(occupancy_almost_zero and no_noise)
or not gdacs_above_threshold or
(self.gdac_lower_limit and
self.register_utils.get_gdac() < self.gdac_lower_limit) or
(min_gdac_with_occupancy and
self.register_utils.get_gdac() <= min_gdac_with_occupancy)
or not no_noise
) and len(
self.gdac_tune_bits
) >= last_good_gdac_scan_step + 2: # min. 2 bits for bin search
self.set_gdac_bit(
gdac_bit, bit_value=0,
send_command=False) # clear current tuning bit
if gdac_tune_bits_permutation + 1 == 2**last_good_gdac_scan_step: # next permutation step
gdac_tune_bits_permutation = 0
last_good_gdac_scan_step += 1
else:
gdac_tune_bits_permutation += 1
gdac_tune_bits.extend(
self.gdac_tune_bits[last_good_gdac_scan_step:]
) # repeat all scan steps from last bit
elif gdac_bit == 0 and not additional_scan_ongoing: # scan bit 0 = 1
additional_scan_ongoing = True
last_occ_array_sel_pixels = occ_array_sel_pixels.copy()
last_occ_array_desel_pixels = occ_array_desel_pixels.copy()
last_median_occupancy = median_occupancy
gdac_tune_bits.append(
0) # the last tune bit has to be scanned twice
elif gdac_bit == 0 and additional_scan_ongoing: # scan bit 0 = 0
additional_scan_ongoing = False
logging.info(
'Measured %.2f with bit 0 = 0 with and %.2f with bit 0 = 1',
median_occupancy, last_median_occupancy)
if (abs(median_occupancy - self.n_injections_gdac / 2.0) >=
abs(last_median_occupancy -
self.n_injections_gdac / 2.0)
) or (
last_median_occupancy >=
self.n_injections_gdac / 2.0
): # if bit 0 = 0 is worse than bit 0 = 1, so go back
logging.info('Set bit 0 = 1')
self.set_gdac_bit(
0, bit_value=1,
send_command=True) # write GDAC value
occ_array_sel_pixels = last_occ_array_sel_pixels.copy()
occ_array_desel_pixels = last_occ_array_desel_pixels.copy(
)
median_occupancy = last_median_occupancy
else:
logging.info('Keep bit 0 = 0')
else: # regular GDAC scan step
# GDAC too low, no hits
if (self.gdac_lower_limit and
self.register_utils.get_gdac() < self.gdac_lower_limit
) or (min_gdac_with_occupancy
and self.register_utils.get_gdac() <=
min_gdac_with_occupancy) or not no_noise:
logging.info(
'Median = %.2f > %.2f, GDAC possibly too low, keep bit %d = 1',
median_occupancy, self.n_injections_gdac / 2.0,
gdac_bit)
# GDAC too high, less hits, decrease GDAC
elif median_occupancy < self.n_injections_gdac / 2.0: # set GDAC bit to 0 if the occupancy is too low, thus decrease threshold
logging.info('Median = %.2f < %.2f, set bit %d = 0',
median_occupancy,
self.n_injections_gdac / 2.0, gdac_bit)
self.set_gdac_bit(
gdac_bit, bit_value=0, send_command=False
) # do not write, might be too low, do this in next iteration
# GDAC too low, more hits, increase GDAC
else:
gdacs_above_threshold.append(
self.register_utils.get_gdac())
logging.info('Median = %.2f > %.2f, keep bit %d = 1',
median_occupancy,
self.n_injections_gdac / 2.0, gdac_bit)
if not self.stop_run.is_set():
# select best GDAC value
sorted_indices = np.argsort(np.array(gdac_values))
occupancy_sorted = np.array(gdac_occupancies)[sorted_indices]
gdac_sorted = np.sort(gdac_values)
try:
diff_occupancy = occupancy_sorted[1:] - occupancy_sorted[:-1]
gdac_min_idx = np.where(diff_occupancy > 0)[0][-1] + 1
except IndexError:
gdac_min_idx = None
occupancy_sorted_sel = occupancy_sorted[gdac_min_idx:]
best_index_sel = np.abs(
np.array(occupancy_sorted_sel[::-1]) -
self.n_injections_gdac / 2.0).argmin()
best_index = sorted_indices[gdac_min_idx:][::-1][best_index_sel]
gdac_best = gdac_values[best_index]
median_occupancy = gdac_occupancies[best_index]
# for plotting
self.occ_array_sel_pixels_best = gdac_occ_array_sel_pixels[
best_index]
self.occ_array_desel_pixels_best = gdac_occ_array_desel_pixels[
best_index]
if gdac_best != self.register_utils.get_gdac():
logging.info(
"Binary search converged to non-optimal value, apply best GDAC value, change GDAC from %d to %d",
self.register_utils.get_gdac(), gdac_best)
self.register_utils.set_gdac(gdac_best, send_command=False)
self.gdac_best = self.register_utils.get_gdac()
if abs(median_occupancy - self.n_injections_gdac / 2.0) > abs(
self.n_injections_gdac * 0.01 *
self.max_delta_threshold) and not self.stop_run.is_set():
if np.all((((self.gdac_best & (1 << np.arange(
self.register.global_registers['Vthin_AltFine']
['bitlength'] + self.register.
global_registers['Vthin_AltFine']['bitlength'])))) > 0
).astype(int)[self.gdac_tune_bits] == 1):
if self.fail_on_warning:
raise RuntimeWarning(
'Selected GDAC bits reached maximum value')
else:
logging.warning(
'Selected GDAC bits reached maximum value')
elif np.all((((self.gdac_best & (1 << np.arange(
self.register.global_registers['Vthin_AltFine']
['bitlength'] + self.register.
global_registers['Vthin_AltFine']['bitlength'])))) > 0
).astype(int)[self.gdac_tune_bits] == 0):
if self.fail_on_warning:
raise RuntimeWarning(
'Selected GDAC bits reached minimum value')
else:
logging.warning(
'Selected GDAC bits reached minimum value')
else:
if self.fail_on_warning:
raise RuntimeWarning(
'Global threshold tuning failed. Delta threshold = %.2f > %.2f. Vthin_AltCoarse / Vthin_AltFine = %d / %d'
% (abs(median_occupancy -
self.n_injections_gdac / 2.0),
abs(self.n_injections_gdac * 0.01 *
self.max_delta_threshold),
self.register.get_global_register_value(
"Vthin_AltCoarse"),
self.register.get_global_register_value(
"Vthin_AltFine")))
else:
logging.warning(
'Global threshold tuning failed. Delta threshold = %.2f > %.2f. Vthin_AltCoarse / Vthin_AltFine = %d / %d',
abs(median_occupancy -
self.n_injections_gdac / 2.0),
abs(self.n_injections_gdac * 0.01 *
self.max_delta_threshold),
self.register.get_global_register_value(
"Vthin_AltCoarse"),
self.register.get_global_register_value(
"Vthin_AltFine"))
else:
logging.info(
'Tuned GDAC to Vthin_AltCoarse / Vthin_AltFine = %d / %d',
self.register.get_global_register_value("Vthin_AltCoarse"),
self.register.get_global_register_value("Vthin_AltFine"))
def scan(self):
if not self.plots_filename:
self.plots_filename = PdfPages(self.output_filename + '.pdf')
self.close_plots = True
else:
self.close_plots = False
enable_mask_steps = [0] # one mask step to increase speed, no effect on precision
cal_lvl1_command = self.register.get_commands("CAL")[0] + self.register.get_commands("zeros", length=40)[0] + self.register.get_commands("LV1")[0] + self.register.get_commands("zeros", mask_steps=self.mask_steps)[0]
self.write_target_charge()
for feedback_bit in self.feedback_tune_bits: # reset all GDAC bits
self.set_prmp_vbpf_bit(feedback_bit, bit_value=0)
additional_scan = True
last_bit_result = self.n_injections_feedback
tot_mean_best = 0
feedback_best = self.register.get_global_register_value("PrmpVbpf")
for feedback_bit in self.feedback_tune_bits:
if additional_scan:
self.set_prmp_vbpf_bit(feedback_bit)
logging.info('PrmpVbpf setting: %d, bit %d = 1', self.register.get_global_register_value("PrmpVbpf"), feedback_bit)
else:
self.set_prmp_vbpf_bit(feedback_bit, bit_value=0)
logging.info('PrmpVbpf setting: %d, bit %d = 0', self.register.get_global_register_value("PrmpVbpf"), feedback_bit)
scan_parameter_value = self.register.get_global_register_value("PrmpVbpf")
with self.readout(PrmpVbpf=scan_parameter_value, reset_sram_fifo=True, fill_buffer=True, clear_buffer=True, callback=self.handle_data):
scan_loop(self, cal_lvl1_command, repeat_command=self.n_injections_feedback, mask_steps=self.mask_steps, enable_mask_steps=enable_mask_steps, enable_double_columns=None, same_mask_for_all_dc=True, eol_function=None, digital_injection=False, enable_shift_masks=self.enable_shift_masks, disable_shift_masks=self.disable_shift_masks, restore_shift_masks=True, mask=None, double_column_correction=self.pulser_dac_correction)
tots = convert_data_array(data_array_from_data_iterable(self.fifo_readout.data), filter_func=is_data_record, converter_func=get_tot_array_from_data_record_array)
mean_tot = np.mean(tots)
if np.isnan(mean_tot):
logging.error("No hits, ToT calculation not possible, tuning will fail")
if abs(mean_tot - self.target_tot) < abs(tot_mean_best - self.target_tot):
tot_mean_best = mean_tot
feedback_best = self.register.get_global_register_value("PrmpVbpf")
logging.info('Mean ToT = %f', mean_tot)
self.tot_array, _ = np.histogram(a=tots, range=(0, 16), bins=16)
if self.plot_intermediate_steps:
plot_tot(hist=self.tot_array, title='ToT distribution (PrmpVbpf ' + str(scan_parameter_value) + ')', filename=self.plots_filename)
if abs(mean_tot - self.target_tot) < self.max_delta_tot and feedback_bit > 0: # abort if good value already found to save time
logging.info('Good result already achieved, skipping missing bits')
break
if feedback_bit > 0 and mean_tot < self.target_tot:
self.set_prmp_vbpf_bit(feedback_bit, bit_value=0)
logging.info('Mean ToT = %f < %d ToT, set bit %d = 0', mean_tot, self.target_tot, feedback_bit)
if feedback_bit == 0:
if additional_scan: # scan bit = 0 with the correct value again
additional_scan = False
last_bit_result = mean_tot
self.feedback_tune_bits.append(0) # bit 0 has to be scanned twice
else:
logging.info('Scanned bit 0 = 0 with %f instead of %f for scanned bit 0 = 1', mean_tot, last_bit_result)
if(abs(mean_tot - self.target_tot) > abs(last_bit_result - self.target_tot)): # if bit 0 = 0 is worse than bit 0 = 1, so go back
self.set_prmp_vbpf_bit(feedback_bit, bit_value=1)
mean_tot = last_bit_result
logging.info('Set bit 0 = 1')
else:
logging.info('Set bit 0 = 0')
if abs(mean_tot - self.target_tot) > abs(tot_mean_best - self.target_tot):
logging.info("Binary search converged to non optimal value, take best measured value instead")
mean_tot = tot_mean_best
self.register.set_global_register_value("PrmpVbpf", feedback_best)
if self.register.get_global_register_value("PrmpVbpf") == 0 or self.register.get_global_register_value("PrmpVbpf") == 254:
logging.warning('PrmpVbpf reached minimum/maximum value')
if self.fail_on_warning:
raise RuntimeWarning('PrmpVbpf reached minimum/maximum value')
if abs(mean_tot - self.target_tot) > 2 * self.max_delta_tot:
logging.warning('Global feedback tuning failed. Delta ToT = %f > %f. PrmpVbpf = %d', abs(mean_tot - self.target_tot), self.max_delta_tot, self.register.get_global_register_value("PrmpVbpf"))
if self.fail_on_warning:
raise RuntimeWarning('Global feedback tuning failed.')
else:
logging.info('Tuned PrmpVbpf to %d', self.register.get_global_register_value("PrmpVbpf"))
self.feedback_best = self.register.get_global_register_value("PrmpVbpf")
def scan(self):
if not self.plots_filename:
self.plots_filename = PdfPages(self.output_filename + '.pdf')
self.close_plots = True
else:
self.close_plots = False
cal_lvl1_command = self.register.get_commands(
"CAL")[0] + self.register.get_commands(
"zeros", length=40)[0] + self.register.get_commands("LV1")[0]
self.write_target_threshold()
for gdac_bit in self.gdac_tune_bits: # reset all GDAC bits
self.set_gdac_bit(gdac_bit, bit_value=0, send_command=False)
def bits_set(int_type):
int_type = int(int_type)
position = 0
bits_set = []
while (int_type):
if (int_type & 1):
bits_set.append(position)
position += 1
int_type = int_type >> 1
return bits_set
# calculate selected pixels from the mask and the disabled columns
select_mask_array = np.zeros(shape=(80, 336), dtype=np.uint8)
self.occ_array_sel_pixels_best = select_mask_array.copy()
self.occ_array_desel_pixels_best = select_mask_array.copy()
if not self.enable_mask_steps_gdac:
self.enable_mask_steps_gdac = range(self.mask_steps)
for mask_step in self.enable_mask_steps_gdac:
select_mask_array += make_pixel_mask(steps=self.mask_steps,
shift=mask_step)
for column in bits_set(
self.register.get_global_register_value("DisableColumnCnfg")):
logging.info('Deselect double column %d' % column)
select_mask_array[column, :] = 0
additional_scan = True
additional_scan_ongoing = False
occupancy_best = 0.0
last_good_gdac_bit = self.gdac_tune_bits[0]
last_good_gdac_scan_step = 0
gdac_tune_bits_permutation = 0
gdac_best = self.register_utils.get_gdac()
gdac_tune_bits = self.gdac_tune_bits[:]
min_gdac_with_occupancy = None
for gdac_scan_step, gdac_bit in enumerate(gdac_tune_bits):
if additional_scan:
self.set_gdac_bit(gdac_bit, bit_value=1, send_command=True)
scan_parameter_value = (
self.register.get_global_register_value("Vthin_AltCoarse")
<< 8
) + self.register.get_global_register_value("Vthin_AltFine")
logging.info('GDAC setting: %d, set bit %d = 1',
scan_parameter_value, gdac_bit)
else:
self.set_gdac_bit(gdac_bit, bit_value=0, send_command=True)
scan_parameter_value = (
self.register.get_global_register_value("Vthin_AltCoarse")
<< 8
) + self.register.get_global_register_value("Vthin_AltFine")
logging.info('GDAC setting: %d, set bit %d = 0',
scan_parameter_value, gdac_bit)
with self.readout(GDAC=scan_parameter_value,
reset_sram_fifo=True,
fill_buffer=True,
clear_buffer=True,
callback=self.handle_data):
scan_loop(self,
command=cal_lvl1_command,
repeat_command=self.n_injections_gdac,
mask_steps=self.mask_steps,
enable_mask_steps=self.enable_mask_steps_gdac,
enable_double_columns=None,
same_mask_for_all_dc=self.same_mask_for_all_dc,
eol_function=None,
digital_injection=False,
enable_shift_masks=self.enable_shift_masks,
disable_shift_masks=self.disable_shift_masks,
restore_shift_masks=True,
mask=None,
double_column_correction=self.pulser_dac_correction)
occupancy_array, _, _ = np.histogram2d(*convert_data_array(
data_array_from_data_iterable(self.fifo_readout.data),
filter_func=logical_and(is_fe_word, is_data_record),
converter_func=get_col_row_array_from_data_record_array),
bins=(80, 336),
range=[[1, 80], [1, 336]])
occ_array_sel_pixels = np.ma.array(
occupancy_array,
mask=np.logical_not(np.ma.make_mask(select_mask_array))
) # take only selected pixel into account by using the mask
occ_array_desel_pixels = np.ma.array(
occupancy_array, mask=np.ma.make_mask(select_mask_array)
) # take only de-selected pixel into account by using the inverted mask
median_occupancy = np.ma.median(occ_array_sel_pixels)
noise_occupancy = np.ma.median(occ_array_desel_pixels)
occupancy_almost_zero = np.allclose(median_occupancy, 0)
no_noise = np.allclose(noise_occupancy, 0)
if abs(median_occupancy - self.n_injections_gdac /
2) < abs(occupancy_best - self.n_injections_gdac / 2):
occupancy_best = median_occupancy
gdac_best = self.register_utils.get_gdac()
self.occ_array_sel_pixels_best = occ_array_sel_pixels.copy()
self.occ_array_desel_pixels_best = occ_array_desel_pixels.copy(
)
if self.plot_intermediate_steps:
plot_three_way(self.occ_array_sel_pixel.transpose(),
title="Occupancy (GDAC " +
str(scan_parameter_value) +
" with tuning bit " + str(gdac_bit) + ")",
x_axis_title='Occupancy',
filename=self.plots_filename,
maximum=self.n_injections_gdac)
if not occupancy_almost_zero and no_noise:
if min_gdac_with_occupancy is None:
min_gdac_with_occupancy = self.register_utils.get_gdac()
else:
min_gdac_with_occupancy = min(
min_gdac_with_occupancy,
self.register_utils.get_gdac())
if gdac_bit > 0:
# GDAC too low, no hits
if occupancy_almost_zero and no_noise and self.register_utils.get_gdac(
) < min_gdac_with_occupancy:
logging.info(
'Median = %.2f > %.2f, GDAC possibly too low, keep bit %d = 1',
median_occupancy, self.n_injections_gdac / 2, gdac_bit)
# GDAC too high, less hits, decrease GDAC
elif no_noise and median_occupancy < (
self.n_injections_gdac / 2
): # set GDAC bit to 0 if the occupancy is too low, thus decrease threshold
try:
next_gdac_bit = gdac_tune_bits[gdac_scan_step + 1]
except IndexError:
next_gdac_bit = None
# check if new value is below lower limit
if self.gdac_lower_limit and (
next_gdac_bit is not None
and self.register_utils.get_gdac() - 2**gdac_bit +
2**next_gdac_bit < self.gdac_lower_limit) or (
next_gdac_bit is None
and self.register_utils.get_gdac() -
2**gdac_bit < self.gdac_lower_limit):
logging.info(
'Median = %.2f < %.2f, reaching lower GDAC limit, keep bit %d = 1',
median_occupancy, self.n_injections_gdac / 2,
gdac_bit)
else:
logging.info('Median = %.2f < %.2f, set bit %d = 0',
median_occupancy,
self.n_injections_gdac / 2, gdac_bit)
self.set_gdac_bit(
gdac_bit, bit_value=0, send_command=False
) # do not write, might be too low, do this in next iteration
# GDAC too low, more hits
else:
logging.info('Median = %.2f > %.2f, keep bit %d = 1',
median_occupancy, self.n_injections_gdac / 2,
gdac_bit)
elif gdac_bit == 0:
if not additional_scan_ongoing and (
(occupancy_almost_zero and no_noise) or not no_noise
) and len(self.gdac_tune_bits) > last_good_gdac_scan_step + 2:
self.set_gdac_bit(0, bit_value=0,
send_command=False) # turn off LSB
if len(
gdac_tune_bits
) == gdac_scan_step + 1 and gdac_tune_bits_permutation == 0: # min. 2 bits for bin search
self.set_gdac_bit(
last_good_gdac_bit,
bit_value=1,
send_command=False) # always enable highest bit
gdac_tune_bits.extend(
self.gdac_tune_bits[last_good_gdac_scan_step + 1:]
) # repeat all scan stept from last bit
for gdac_clear_bit in self.gdac_tune_bits[:
last_good_gdac_scan_step]:
self.set_gdac_bit(gdac_clear_bit,
bit_value=0,
send_command=False)
if 2**last_good_gdac_scan_step == 1: # last step, cleanup
last_good_gdac_bit = self.gdac_tune_bits[
last_good_gdac_scan_step + 1]
last_good_gdac_scan_step += 1
else:
gdac_tune_bits_permutation += 1
else:
gdac_tune_bits_permutation_header = map(
int,
bin(gdac_tune_bits_permutation)[2:].zfill(
last_good_gdac_scan_step))
for gdac_permutation_bit, gdac_permutation_bit_value in enumerate(
gdac_tune_bits_permutation_header):
self.set_gdac_bit(
self.gdac_tune_bits[gdac_permutation_bit],
bit_value=gdac_permutation_bit_value,
send_command=False)
gdac_tune_bits.extend(
self.gdac_tune_bits[last_good_gdac_scan_step + 1:])
if 2**last_good_gdac_scan_step > gdac_tune_bits_permutation + 1:
gdac_tune_bits_permutation += 1
else: # last step, cleanup
gdac_tune_bits_permutation = 0
last_good_gdac_bit = self.gdac_tune_bits[
last_good_gdac_scan_step + 1]
last_good_gdac_scan_step += 1
elif additional_scan: # scan bit = 0 with the correct value again
additional_scan = False
additional_scan_ongoing = True
last_occ_array_sel_pixels = occ_array_sel_pixels.copy()
last_occ_array_desel_pixels = occ_array_desel_pixels.copy()
gdac_tune_bits.append(
0) # the last tune bit has to be scanned twice
else:
additional_scan_ongoing = False
last_median_occupancy = np.ma.median(
last_occ_array_sel_pixels)
logging.info(
'Measured %.2f with bit 0 = 0 with and %.2f with bit 0 = 1',
median_occupancy, last_median_occupancy)
if abs(median_occupancy - self.n_injections_gdac / 2) > abs(
last_median_occupancy - self.n_injections_gdac / 2
): # if bit 0 = 0 is worse than bit 0 = 1, so go back
logging.info('Set bit 0 = 1')
self.set_gdac_bit(0, bit_value=1, send_command=True)
occ_array_sel_pixels = last_occ_array_sel_pixels.copy()
occ_array_desel_pixels = last_occ_array_desel_pixels.copy(
)
median_occupancy = last_median_occupancy
else:
logging.info('Keep bit 0 = 0')
# select best GDAC value
if abs(occupancy_best -
self.n_injections_gdac / 2) < abs(median_occupancy -
self.n_injections_gdac / 2):
logging.info(
"Binary search converged to non-optimal value, apply best GDAC value, change GDAC from %d to %d",
self.register_utils.get_gdac(), gdac_best)
median_occupancy = occupancy_best
self.register_utils.set_gdac(gdac_best, send_command=False)
self.gdac_best = self.register_utils.get_gdac()
if abs(median_occupancy -
self.n_injections_gdac / 2) > self.max_delta_threshold:
if np.all((((self.gdac_best & (1 << np.arange(
self.register.global_registers['Vthin_AltFine']
['bitlength'] + self.register.global_registers['Vthin_AltFine']
['bitlength'])))) > 0).astype(int)[self.gdac_tune_bits] == 1):
if self.fail_on_warning:
raise RuntimeWarning(
'Selected GDAC bits reached maximum value')
else:
logging.warning('Selected GDAC bits reached maximum value')
elif np.all((((self.gdac_best & (1 << np.arange(
self.register.global_registers['Vthin_AltFine']
['bitlength'] + self.register.global_registers['Vthin_AltFine']
['bitlength'])))) > 0).astype(int)[self.gdac_tune_bits] == 0):
if self.fail_on_warning:
raise RuntimeWarning(
'Selected GDAC bits reached minimum value')
else:
logging.warning('Selected GDAC bits reached minimum value')
else:
if self.fail_on_warning:
raise RuntimeWarning(
'Global threshold tuning failed. Delta threshold = %.2f > %.2f. Vthin_AltCoarse / Vthin_AltFine = %d / %d'
% (abs(median_occupancy - self.n_injections_gdac / 2),
self.max_delta_threshold,
self.register.get_global_register_value(
"Vthin_AltCoarse"),
self.register.get_global_register_value(
"Vthin_AltFine")))
else:
logging.warning(
'Global threshold tuning failed. Delta threshold = %.2f > %.2f. Vthin_AltCoarse / Vthin_AltFine = %d / %d',
abs(median_occupancy - self.n_injections_gdac / 2),
self.max_delta_threshold,
self.register.get_global_register_value(
"Vthin_AltCoarse"),
self.register.get_global_register_value(
"Vthin_AltFine"))
else:
logging.info(
'Tuned GDAC to Vthin_AltCoarse / Vthin_AltFine = %d / %d',
self.register.get_global_register_value("Vthin_AltCoarse"),
self.register.get_global_register_value("Vthin_AltFine"))
def scan(self):
if not self.plots_filename:
self.plots_filename = PdfPages(self.output_filename + ".pdf")
self.close_plots = True
else:
self.close_plots = False
cal_lvl1_command = (
self.register.get_commands("CAL")[0]
+ self.register.get_commands("zeros", length=40)[0]
+ self.register.get_commands("LV1")[0]
+ self.register.get_commands("zeros", mask_steps=self.mask_steps_gdac)[0]
)
self.write_target_threshold()
for gdac_bit in self.gdac_tune_bits: # reset all GDAC bits
self.set_gdac_bit(gdac_bit, bit_value=0, send_command=False)
last_bit_result = self.n_injections_gdac
decreased_threshold = False # needed to determine if the FE is noisy
all_bits_zero = True
def bits_set(int_type):
int_type = int(int_type)
position = 0
bits_set = []
while int_type:
if int_type & 1:
bits_set.append(position)
position += 1
int_type = int_type >> 1
return bits_set
# calculate selected pixels from the mask and the disabled columns
select_mask_array = np.zeros(shape=(80, 336), dtype=np.uint8)
if not self.enable_mask_steps_gdac:
self.enable_mask_steps_gdac = range(self.mask_steps_gdac)
for mask_step in self.enable_mask_steps_gdac:
select_mask_array += make_pixel_mask(steps=self.mask_steps_gdac, shift=mask_step)
for column in bits_set(self.register.get_global_register_value("DisableColumnCnfg")):
logging.info("Deselect double column %d" % column)
select_mask_array[column, :] = 0
additional_scan = True
occupancy_best = 0
gdac_best = self.register_utils.get_gdac()
for gdac_bit in self.gdac_tune_bits:
if additional_scan:
self.set_gdac_bit(gdac_bit)
scan_parameter_value = (
self.register.get_global_register_value("Vthin_AltCoarse") << 8
) + self.register.get_global_register_value("Vthin_AltFine")
logging.info("GDAC setting: %d, bit %d = 1", scan_parameter_value, gdac_bit)
else:
self.set_gdac_bit(gdac_bit, bit_value=0)
scan_parameter_value = (
self.register.get_global_register_value("Vthin_AltCoarse") << 8
) + self.register.get_global_register_value("Vthin_AltFine")
logging.info("GDAC setting: %d, bit %d = 0", scan_parameter_value, gdac_bit)
with self.readout(
GDAC=scan_parameter_value,
reset_sram_fifo=True,
fill_buffer=True,
clear_buffer=True,
callback=self.handle_data,
):
scan_loop(
self,
cal_lvl1_command,
repeat_command=self.n_injections_gdac,
mask_steps=self.mask_steps_gdac,
enable_mask_steps=self.enable_mask_steps_gdac,
enable_double_columns=None,
same_mask_for_all_dc=True,
eol_function=None,
digital_injection=False,
enable_shift_masks=self.enable_shift_masks,
disable_shift_masks=self.disable_shift_masks,
restore_shift_masks=True,
mask=None,
double_column_correction=self.pulser_dac_correction,
)
occupancy_array, _, _ = np.histogram2d(
*convert_data_array(
data_array_from_data_iterable(self.fifo_readout.data),
filter_func=is_data_record,
converter_func=get_col_row_array_from_data_record_array,
),
bins=(80, 336),
range=[[1, 80], [1, 336]]
)
self.occ_array_sel_pixel = np.ma.array(
occupancy_array, mask=np.logical_not(np.ma.make_mask(select_mask_array))
) # take only selected pixel into account by creating a mask
median_occupancy = np.ma.median(self.occ_array_sel_pixel)
if abs(median_occupancy - self.n_injections_gdac / 2) < abs(occupancy_best - self.n_injections_gdac / 2):
occupancy_best = median_occupancy
gdac_best = self.register_utils.get_gdac()
if self.plot_intermediate_steps:
plot_three_way(
self.occ_array_sel_pixel.transpose(),
title="Occupancy (GDAC " + str(scan_parameter_value) + " with tuning bit " + str(gdac_bit) + ")",
x_axis_title="Occupancy",
filename=self.plots_filename,
maximum=self.n_injections_gdac,
)
if (
abs(median_occupancy - self.n_injections_gdac / 2) < self.max_delta_threshold and gdac_bit > 0
): # abort if good value already found to save time
logging.info(
"Median = %f, good result already achieved (median - Ninj/2 < %f), skipping not varied bits",
median_occupancy,
self.max_delta_threshold,
)
break
if median_occupancy == 0 and decreased_threshold and all_bits_zero:
logging.info("Chip may be noisy")
if gdac_bit > 0:
if (
median_occupancy < self.n_injections_gdac / 2
): # set GDAC bit to 0 if the occupancy is too lowm, thus decrease threshold
logging.info(
"Median = %f < %f, set bit %d = 0", median_occupancy, self.n_injections_gdac / 2, gdac_bit
)
self.set_gdac_bit(gdac_bit, bit_value=0)
decreased_threshold = True
else: # set GDAC bit to 1 if the occupancy is too high, thus increase threshold
logging.info(
"Median = %f > %f, leave bit %d = 1", median_occupancy, self.n_injections_gdac / 2, gdac_bit
)
decreased_threshold = False
all_bits_zero = False
elif gdac_bit == 0:
if additional_scan: # scan bit = 0 with the correct value again
additional_scan = False
last_bit_result = self.occ_array_sel_pixel.copy()
self.gdac_tune_bits.append(self.gdac_tune_bits[-1]) # the last tune bit has to be scanned twice
else:
last_bit_result_median = np.median(last_bit_result[select_mask_array > 0])
logging.info("Scanned bit 0 = 0 with %f instead of %f", median_occupancy, last_bit_result_median)
if abs(median_occupancy - self.n_injections_gdac / 2) > abs(
last_bit_result_median - self.n_injections_gdac / 2
): # if bit 0 = 0 is worse than bit 0 = 1, so go back
self.set_gdac_bit(gdac_bit, bit_value=1)
logging.info("Set bit 0 = 1")
self.occ_array_sel_pixel = last_bit_result
median_occupancy = np.ma.median(self.occ_array_sel_pixel)
else:
logging.info("Set bit 0 = 0")
if abs(occupancy_best - self.n_injections_gdac / 2) < abs(
median_occupancy - self.n_injections_gdac / 2
):
logging.info("Binary search converged to non optimal value, take best measured value instead")
median_occupancy = occupancy_best
self.register_utils.set_gdac(gdac_best, send_command=False)
self.gdac_best = self.register_utils.get_gdac()
if np.all((((self.gdac_best & (1 << np.arange(16)))) > 0).astype(int)[self.gdac_tune_bits[:-2]] == 1):
logging.warning("Selected GDAC bits reached maximum value")
elif np.all((((self.gdac_best & (1 << np.arange(16)))) > 0).astype(int)[self.gdac_tune_bits] == 0):
logging.warning("Selected GDAC bits reached minimum value")
if abs(median_occupancy - self.n_injections_gdac / 2) > 2 * self.max_delta_threshold:
logging.warning(
"Global threshold tuning failed. Delta threshold = %f > %f. Vthin_AltCoarse / Vthin_AltFine = %d / %d",
abs(median_occupancy - self.n_injections_gdac / 2),
self.max_delta_threshold,
self.register.get_global_register_value("Vthin_AltCoarse"),
self.register.get_global_register_value("Vthin_AltFine"),
)
else:
logging.info(
"Tuned GDAC to Vthin_AltCoarse / Vthin_AltFine = %d / %d",
self.register.get_global_register_value("Vthin_AltCoarse"),
self.register.get_global_register_value("Vthin_AltFine"),
)
self.gdac_best = self.register_utils.get_gdac()
def scan(self):
if not self.plots_filename:
self.plots_filename = PdfPages(self.output_filename + '.pdf')
self.close_plots = True
else:
self.close_plots = False
mask_steps = 3
enable_mask_steps = []
cal_lvl1_command = self.register.get_commands("CAL")[0] + self.register.get_commands("zeros", length=40)[0] + self.register.get_commands("LV1")[0] + self.register.get_commands("zeros", mask_steps=mask_steps)[0]
self.write_target_threshold()
additional_scan = True
lastBitResult = np.zeros(shape=self.register.get_pixel_register_value("TDAC").shape, dtype=self.register.get_pixel_register_value("TDAC").dtype)
self.set_start_tdac()
self.occupancy_best = np.empty(shape=(80, 336)) # array to store the best occupancy (closest to Ninjections/2) of the pixel
self.occupancy_best.fill(self.n_injections_tdac)
self.tdac_mask_best = self.register.get_pixel_register_value("TDAC")
for scan_parameter_value, tdac_bit in enumerate(self.tdac_tune_bits):
if additional_scan:
self.set_tdac_bit(tdac_bit)
logging.info('TDAC setting: bit %d = 1', tdac_bit)
else:
self.set_tdac_bit(tdac_bit, bit_value=0)
logging.info('TDAC setting: bit %d = 0', tdac_bit)
self.write_tdac_config()
with self.readout(TDAC=scan_parameter_value, reset_sram_fifo=True, fill_buffer=True, clear_buffer=True, callback=self.handle_data):
scan_loop(self, cal_lvl1_command, repeat_command=self.n_injections_tdac, mask_steps=mask_steps, enable_mask_steps=enable_mask_steps, enable_double_columns=None, same_mask_for_all_dc=True, eol_function=None, digital_injection=False, enable_shift_masks=self.enable_shift_masks, disable_shift_masks=self.disable_shift_masks, restore_shift_masks=True, mask=None, double_column_correction=self.pulser_dac_correction)
occupancy_array, _, _ = np.histogram2d(*convert_data_array(data_array_from_data_iterable(self.fifo_readout.data), filter_func=is_data_record, converter_func=get_col_row_array_from_data_record_array), bins=(80, 336), range=[[1, 80], [1, 336]])
select_better_pixel_mask = abs(occupancy_array - self.n_injections_tdac / 2) <= abs(self.occupancy_best - self.n_injections_tdac / 2)
pixel_with_too_high_occupancy_mask = occupancy_array > self.n_injections_tdac / 2
self.occupancy_best[select_better_pixel_mask] = occupancy_array[select_better_pixel_mask]
if self.plot_intermediate_steps:
plotThreeWay(occupancy_array.transpose(), title="Occupancy (TDAC tuning bit " + str(tdac_bit) + ")", x_axis_title='Occupancy', filename=self.plots_filename, maximum=self.n_injections_tdac)
tdac_mask = self.register.get_pixel_register_value("TDAC")
self.tdac_mask_best[select_better_pixel_mask] = tdac_mask[select_better_pixel_mask]
if tdac_bit > 0:
tdac_mask[pixel_with_too_high_occupancy_mask] = tdac_mask[pixel_with_too_high_occupancy_mask] & ~(1 << tdac_bit)
self.register.set_pixel_register_value("TDAC", tdac_mask)
if tdac_bit == 0:
if additional_scan: # scan bit = 0 with the correct value again
additional_scan = False
lastBitResult = occupancy_array.copy()
self.tdac_tune_bits.append(0) # bit 0 has to be scanned twice
else:
tdac_mask[abs(occupancy_array - self.n_injections_tdac / 2) > abs(lastBitResult - self.n_injections_tdac / 2)] = tdac_mask[abs(occupancy_array - self.n_injections_tdac / 2) > abs(lastBitResult - self.n_injections_tdac / 2)] | (1 << tdac_bit)
occupancy_array[abs(occupancy_array - self.n_injections_tdac / 2) > abs(lastBitResult - self.n_injections_tdac / 2)] = lastBitResult[abs(occupancy_array - self.n_injections_tdac / 2) > abs(lastBitResult - self.n_injections_tdac / 2)]
self.occupancy_best[abs(occupancy_array - self.n_injections_tdac / 2) <= abs(self.occupancy_best - self.n_injections_tdac / 2)] = occupancy_array[abs(occupancy_array - self.n_injections_tdac / 2) <= abs(self.occupancy_best - self.n_injections_tdac / 2)]
self.tdac_mask_best[abs(occupancy_array - self.n_injections_tdac / 2) <= abs(self.occupancy_best - self.n_injections_tdac / 2)] = tdac_mask[abs(occupancy_array - self.n_injections_tdac / 2) <= abs(self.occupancy_best - self.n_injections_tdac / 2)]
self.register.set_pixel_register_value("TDAC", self.tdac_mask_best) # set value for meta scan
self.write_tdac_config()
def scan(self):
if not self.plots_filename:
self.plots_filename = PdfPages(self.output_filename + '.pdf')
self.close_plots = True
else:
self.close_plots = False
cal_lvl1_command = self.register.get_commands(
"CAL")[0] + self.register.get_commands(
"zeros", length=40)[0] + self.register.get_commands(
"LV1")[0] + self.register.get_commands(
"zeros", mask_steps=self.mask_steps_gdac)[0]
self.write_target_threshold()
for gdac_bit in self.gdac_tune_bits: # reset all GDAC bits
self.set_gdac_bit(gdac_bit, bit_value=0, send_command=False)
last_bit_result = self.n_injections_gdac
decreased_threshold = False # needed to determine if the FE is noisy
all_bits_zero = True
def bits_set(int_type):
int_type = int(int_type)
position = 0
bits_set = []
while (int_type):
if (int_type & 1):
bits_set.append(position)
position += 1
int_type = int_type >> 1
return bits_set
# calculate selected pixels from the mask and the disabled columns
select_mask_array = np.zeros(shape=(80, 336), dtype=np.uint8)
if not self.enable_mask_steps_gdac:
self.enable_mask_steps_gdac = range(self.mask_steps_gdac)
for mask_step in self.enable_mask_steps_gdac:
select_mask_array += make_pixel_mask(steps=self.mask_steps_gdac,
shift=mask_step)
for column in bits_set(
self.register.get_global_register_value("DisableColumnCnfg")):
logging.info('Deselect double column %d' % column)
select_mask_array[column, :] = 0
additional_scan = True
occupancy_best = 0
gdac_best = self.register_utils.get_gdac()
for gdac_bit in self.gdac_tune_bits:
if additional_scan:
self.set_gdac_bit(gdac_bit)
scan_parameter_value = (
self.register.get_global_register_value("Vthin_AltCoarse")
<< 8
) + self.register.get_global_register_value("Vthin_AltFine")
logging.info('GDAC setting: %d, bit %d = 1',
scan_parameter_value, gdac_bit)
else:
self.set_gdac_bit(gdac_bit, bit_value=0)
scan_parameter_value = (
self.register.get_global_register_value("Vthin_AltCoarse")
<< 8
) + self.register.get_global_register_value("Vthin_AltFine")
logging.info('GDAC setting: %d, bit %d = 0',
scan_parameter_value, gdac_bit)
with self.readout(GDAC=scan_parameter_value,
reset_sram_fifo=True,
fill_buffer=True,
clear_buffer=True,
callback=self.handle_data):
scan_loop(self,
cal_lvl1_command,
repeat_command=self.n_injections_gdac,
mask_steps=self.mask_steps_gdac,
enable_mask_steps=self.enable_mask_steps_gdac,
enable_double_columns=None,
same_mask_for_all_dc=True,
eol_function=None,
digital_injection=False,
enable_shift_masks=self.enable_shift_masks,
disable_shift_masks=self.disable_shift_masks,
restore_shift_masks=True,
mask=None,
double_column_correction=self.pulser_dac_correction)
occupancy_array, _, _ = np.histogram2d(*convert_data_array(
data_array_from_data_iterable(self.fifo_readout.data),
filter_func=is_data_record,
converter_func=get_col_row_array_from_data_record_array),
bins=(80, 336),
range=[[1, 80], [1, 336]])
self.occ_array_sel_pixel = np.ma.array(
occupancy_array,
mask=np.logical_not(np.ma.make_mask(select_mask_array))
) # take only selected pixel into account by creating a mask
median_occupancy = np.ma.median(self.occ_array_sel_pixel)
if abs(median_occupancy - self.n_injections_gdac /
2) < abs(occupancy_best - self.n_injections_gdac / 2):
occupancy_best = median_occupancy
gdac_best = self.register_utils.get_gdac()
if self.plot_intermediate_steps:
plot_three_way(self.occ_array_sel_pixel.transpose(),
title="Occupancy (GDAC " +
str(scan_parameter_value) +
" with tuning bit " + str(gdac_bit) + ")",
x_axis_title='Occupancy',
filename=self.plots_filename,
maximum=self.n_injections_gdac)
if abs(
median_occupancy - self.n_injections_gdac / 2
) < self.max_delta_threshold and gdac_bit > 0: # abort if good value already found to save time
logging.info(
'Median = %f, good result already achieved (median - Ninj/2 < %f), skipping not varied bits',
median_occupancy, self.max_delta_threshold)
break
if median_occupancy == 0 and decreased_threshold and all_bits_zero:
logging.info('Chip may be noisy')
if gdac_bit > 0:
if (
median_occupancy < self.n_injections_gdac / 2
): # set GDAC bit to 0 if the occupancy is too lowm, thus decrease threshold
logging.info('Median = %f < %f, set bit %d = 0',
median_occupancy, self.n_injections_gdac / 2,
gdac_bit)
self.set_gdac_bit(gdac_bit, bit_value=0)
decreased_threshold = True
else: # set GDAC bit to 1 if the occupancy is too high, thus increase threshold
logging.info('Median = %f > %f, leave bit %d = 1',
median_occupancy, self.n_injections_gdac / 2,
gdac_bit)
decreased_threshold = False
all_bits_zero = False
elif gdac_bit == 0:
if additional_scan: # scan bit = 0 with the correct value again
additional_scan = False
last_bit_result = self.occ_array_sel_pixel.copy()
self.gdac_tune_bits.append(
self.gdac_tune_bits[-1]
) # the last tune bit has to be scanned twice
else:
last_bit_result_median = np.median(
last_bit_result[select_mask_array > 0])
logging.info('Scanned bit 0 = 0 with %f instead of %f',
median_occupancy, last_bit_result_median)
if abs(median_occupancy - self.n_injections_gdac / 2) > abs(
last_bit_result_median - self.n_injections_gdac / 2
): # if bit 0 = 0 is worse than bit 0 = 1, so go back
self.set_gdac_bit(gdac_bit, bit_value=1)
logging.info('Set bit 0 = 1')
self.occ_array_sel_pixel = last_bit_result
median_occupancy = np.ma.median(
self.occ_array_sel_pixel)
else:
logging.info('Set bit 0 = 0')
if abs(occupancy_best - self.n_injections_gdac / 2) < abs(
median_occupancy - self.n_injections_gdac / 2):
logging.info(
"Binary search converged to non optimal value, take best measured value instead"
)
median_occupancy = occupancy_best
self.register_utils.set_gdac(gdac_best,
send_command=False)
self.gdac_best = self.register_utils.get_gdac()
if np.all((((self.gdac_best & (1 << np.arange(16)))) > 0
).astype(int)[self.gdac_tune_bits[:-2]] == 1):
logging.warning('Selected GDAC bits reached maximum value')
elif np.all((((self.gdac_best & (1 << np.arange(16)))) > 0
).astype(int)[self.gdac_tune_bits] == 0):
logging.warning('Selected GDAC bits reached minimum value')
if abs(median_occupancy -
self.n_injections_gdac / 2) > 2 * self.max_delta_threshold:
logging.warning(
'Global threshold tuning failed. Delta threshold = %f > %f. Vthin_AltCoarse / Vthin_AltFine = %d / %d',
abs(median_occupancy - self.n_injections_gdac / 2),
self.max_delta_threshold,
self.register.get_global_register_value("Vthin_AltCoarse"),
self.register.get_global_register_value("Vthin_AltFine"))
else:
logging.info(
'Tuned GDAC to Vthin_AltCoarse / Vthin_AltFine = %d / %d',
self.register.get_global_register_value("Vthin_AltCoarse"),
self.register.get_global_register_value("Vthin_AltFine"))
self.gdac_best = self.register_utils.get_gdac()
def scan(self):
self.start_condition_triggered = False # set to true if the start condition is true once
self.stop_condition_triggered = False # set to true if the stop condition is true once
self.start_at = 0.01 # if more than start_at*activated_pixel see at least one hit the precise scanning is started
self.stop_at = 0.95 # if more than stop_at*activated_pixel see the maximum numbers of injection, the scan is stopped
self.record_data = False # set to true to activate data storage, so far not everything is recorded to ease data analysis
scan_parameter_range = [0, (2 ** self.register.global_registers['PlsrDAC']['bitlength'] - 1)]
if self.scan_parameters.PlsrDAC[0]:
scan_parameter_range[0] = self.scan_parameters.PlsrDAC[0]
if self.scan_parameters.PlsrDAC[1]:
scan_parameter_range[1] = self.scan_parameters.PlsrDAC[1]
logging.info("Scanning %s from %d to %d", 'PlsrDAC', scan_parameter_range[0], scan_parameter_range[1])
self.scan_parameter_value = scan_parameter_range[0] # set to start value
self.search_distance = self.search_distance
self.data_points = 0 # counter variable to count the data points already recorded, have to be at least minimum_data_ponts
# calculate DCs to scan from the columns to ignore
enable_double_columns = range(0, 40)
if 1 in self.ignore_columns:
enable_double_columns.remove(0)
if set((78, 79, 80)).issubset(self.ignore_columns):
enable_double_columns.remove(39)
for double_column in range(1, 39):
if set((double_column * 2, (double_column * 2) + 1)).issubset(self.ignore_columns):
enable_double_columns.remove(double_column)
logging.info("Use DCs: %s", str(enable_double_columns))
self.select_arr_columns = range(0, 80)
for column in self.ignore_columns:
self.select_arr_columns.remove(column - 1)
while self.scan_parameter_value <= scan_parameter_range[1]: # scan as long as scan parameter is smaller than defined maximum
if self.stop_run.is_set():
break
commands = []
commands.extend(self.register.get_commands("ConfMode"))
self.register.set_global_register_value('PlsrDAC', self.scan_parameter_value)
commands.extend(self.register.get_commands("WrRegister", name=['PlsrDAC']))
self.register_utils.send_commands(commands)
with self.readout(PlsrDAC=self.scan_parameter_value, reset_sram_fifo=True, fill_buffer=True, clear_buffer=True, callback=self.handle_data if self.record_data else None):
cal_lvl1_command = self.register.get_commands("CAL")[0] + self.register.get_commands("zeros", length=40)[0] + self.register.get_commands("LV1")[0]
scan_loop(self, cal_lvl1_command, repeat_command=self.n_injections, use_delay=True, mask_steps=self.mask_steps, enable_mask_steps=self.enable_mask_steps, enable_double_columns=enable_double_columns, same_mask_for_all_dc=True, eol_function=None, digital_injection=False, enable_shift_masks=self.enable_shift_masks, disable_shift_masks=self.disable_shift_masks, restore_shift_masks=False, mask=invert_pixel_mask(self.register.get_pixel_register_value('Enable')) if self.use_enable_mask else None, double_column_correction=self.pulser_dac_correction)
if not self.start_condition_triggered or self.data_points > self.minimum_data_points: # speed up, only create histograms when needed. Python is much too slow here.
if not self.start_condition_triggered and not self.record_data:
logging.info('Testing for start condition: %s %d', 'PlsrDAC', self.scan_parameter_value)
if not self.stop_condition_triggered and self.record_data:
logging.info('Testing for stop condition: %s %d', 'PlsrDAC', self.scan_parameter_value)
col, row = convert_data_array(data_array_from_data_iterable(self.fifo_readout.data), filter_func=is_data_record, converter_func=get_col_row_array_from_data_record_array)
if np.any(np.logical_and(col < 1, col > 80)) or np.any(np.logical_and(row < 1, row > 336)): # filter bad data records that can happen
logging.warning('There are undefined %d data records (e.g. random data)', np.count_nonzero(np.logical_and(col < 1, col > 80)) + np.count_nonzero(np.logical_and(row < 1, row > 336)))
col, row = col[np.logical_and(col > 0, col <= 80)], row[np.logical_and(row > 0, row < 336)]
occupancy_array = hist_2d_index(col - 1, row - 1, shape=(80, 336))
self.scan_condition(occupancy_array)
# start condition is met for the first time
if self.start_condition_triggered and not self.record_data:
self.scan_parameter_value = self.scan_parameter_value - self.search_distance + self.step_size
if self.scan_parameter_value < 0:
self.scan_parameter_value = 0
logging.info('Starting threshold scan at %s %d', 'PlsrDAC', self.scan_parameter_value)
self.scan_parameter_start = self.scan_parameter_value
self.record_data = True
continue
# saving data
if self.record_data:
self.data_points = self.data_points + 1
# stop condition is met for the first time
if self.stop_condition_triggered and self.record_data:
logging.info('Stopping threshold scan at %s %d', 'PlsrDAC', self.scan_parameter_value)
break
# increase scan parameter value
if not self.start_condition_triggered:
self.scan_parameter_value = self.scan_parameter_value + self.search_distance
else:
self.scan_parameter_value = self.scan_parameter_value + self.step_size
if self.scan_parameter_value >= scan_parameter_range[1]:
logging.warning("Reached maximum of PlsrDAC range... stopping scan")
def scan(self):
self.start_condition_triggered = False # set to true if the start condition is true once
self.stop_condition_triggered = False # set to true if the stop condition is true once
self.start_at = 0.01 # if more than start_at*activated_pixel see at least one hit the precise scanning is started
self.stop_at = 0.95 # if more than stop_at*activated_pixel see the maximum numbers of injection, the scan is stopped
self.record_data = False # set to true to activate data storage, so far not everything is recorded to ease data analysis
scan_parameter_range = [
0, (2**self.register.global_registers['PlsrDAC']['bitlength'])
]
if self.scan_parameters.PlsrDAC[0]:
scan_parameter_range[0] = self.scan_parameters.PlsrDAC[0]
if self.scan_parameters.PlsrDAC[1]:
scan_parameter_range[1] = self.scan_parameters.PlsrDAC[1]
logging.info(
"Scanning %s from %d to %d" %
('PlsrDAC', scan_parameter_range[0], scan_parameter_range[1]))
self.scan_parameter_value = scan_parameter_range[
0] # set to start value
self.search_distance = self.search_distance
self.data_points = 0 # counter variable to count the data points already recorded, have to be at least minimum_data_ponts
# calculate DCs to scan from the columns to ignore
enable_double_columns = range(0, 40)
if 1 in self.ignore_columns:
enable_double_columns.remove(0)
if set((78, 79, 80)).issubset(self.ignore_columns):
enable_double_columns.remove(39)
for double_column in range(1, 39):
if set((double_column * 2,
(double_column * 2) + 1)).issubset(self.ignore_columns):
enable_double_columns.remove(double_column)
logging.info("Use DCs: %s" % str(enable_double_columns))
self.select_arr_columns = range(0, 80)
for column in self.ignore_columns:
self.select_arr_columns.remove(column - 1)
while self.scan_parameter_value <= scan_parameter_range[
1]: # scan as long as scan parameter is smaller than defined maximum
if self.stop_run.is_set():
break
if self.record_data:
logging.info(
"Scan step %d (%s %d)" %
(self.data_points, 'PlsrDAC', self.scan_parameter_value))
commands = []
commands.extend(self.register.get_commands("ConfMode"))
self.register.set_global_register_value('PlsrDAC',
self.scan_parameter_value)
commands.extend(
self.register.get_commands("WrRegister", name=['PlsrDAC']))
self.register_utils.send_commands(commands)
with self.readout(PlsrDAC=self.scan_parameter_value):
cal_lvl1_command = self.register.get_commands(
"CAL")[0] + self.register.get_commands(
"zeros",
length=40)[0] + self.register.get_commands("LV1")[0]
scan_loop(self,
cal_lvl1_command,
repeat_command=self.n_injections,
use_delay=True,
mask_steps=self.mask_steps,
enable_mask_steps=self.enable_mask_steps,
enable_double_columns=enable_double_columns,
same_mask_for_all_dc=True,
eol_function=None,
digital_injection=False,
enable_shift_masks=self.enable_shift_masks,
disable_shift_masks=self.disable_shift_masks,
restore_shift_masks=False,
mask=invert_pixel_mask(
self.register.get_pixel_register_value('Enable'))
if self.use_enable_mask else None,
double_column_correction=self.pulser_dac_correction)
if not self.start_condition_triggered or self.data_points > self.minimum_data_points: # speed up, only create histograms when needed. Python is much too slow here.
if not self.start_condition_triggered and not self.record_data:
logging.info('Testing for start condition: %s %d' %
('PlsrDAC', self.scan_parameter_value))
if not self.stop_condition_triggered and self.record_data:
logging.info('Testing for stop condition: %s %d' %
('PlsrDAC', self.scan_parameter_value))
col, row = convert_data_array(
data_array_from_data_iterable(self.fifo_readout.data),
filter_func=is_data_record,
converter_func=get_col_row_array_from_data_record_array)
# using self written histogrammer in C++
occupancy_array = hist_2d_index(col - 1,
row - 1,
shape=(80, 336))
# using numpy
# occupancy_array = np.histogram2d(col, row, bins=(80, 336), range=[[1, 80], [1, 336]])[0]
self.scan_condition(occupancy_array)
# start condition is met for the first time
if self.start_condition_triggered and not self.record_data:
self.scan_parameter_value = self.scan_parameter_value - self.search_distance + self.step_size
if self.scan_parameter_value < 0:
self.scan_parameter_value = 0
logging.info('Starting threshold scan at %s %d' %
('PlsrDAC', self.scan_parameter_value))
self.scan_parameter_start = self.scan_parameter_value
self.record_data = True
continue
# saving data
if self.record_data:
self.data_points = self.data_points + 1
self.raw_data_file.append(
self.fifo_readout.data,
scan_parameters=self.scan_parameters._asdict())
# stop condition is met for the first time
if self.stop_condition_triggered and self.record_data:
logging.info('Stopping threshold scan at %s %d' %
('PlsrDAC', self.scan_parameter_value))
break
# increase scan parameter value
if not self.start_condition_triggered:
self.scan_parameter_value = self.scan_parameter_value + self.search_distance
else:
self.scan_parameter_value = self.scan_parameter_value + self.step_size
if self.scan_parameter_value >= scan_parameter_range[1]:
logging.warning(
"Reached maximum of PlsrDAC range... stopping scan")
def scan(self):
if not self.plots_filename:
self.plots_filename = PdfPages(self.output_filename + '.pdf')
self.close_plots = True
else:
self.close_plots = False
cal_lvl1_command = self.register.get_commands(
"CAL")[0] + self.register.get_commands(
"zeros", length=40)[0] + self.register.get_commands("LV1")[0]
self.write_target_threshold()
scan_parameter_range = [
(2**self.register.global_registers['Vthin_AltFine']['bitlength']),
0
] # high to low
if self.scan_parameters.GDAC[0]:
scan_parameter_range[0] = self.scan_parameters.GDAC[0]
if self.scan_parameters.GDAC[1]:
scan_parameter_range[1] = self.scan_parameters.GDAC[1]
scan_parameter_range = range(scan_parameter_range[0],
scan_parameter_range[1] - 1,
self.step_size)
logging.info("Scanning %s from %d to %d", 'GDAC',
scan_parameter_range[0], scan_parameter_range[-1])
def bits_set(int_type):
int_type = int(int_type)
position = 0
bits_set = []
while (int_type):
if (int_type & 1):
bits_set.append(position)
position += 1
int_type = int_type >> 1
return bits_set
# calculate selected pixels from the mask and the disabled columns
select_mask_array = np.zeros(shape=(80, 336), dtype=np.uint8)
self.occ_array_sel_pixels_best = select_mask_array.copy()
self.occ_array_desel_pixels_best = select_mask_array.copy()
if not self.enable_mask_steps_gdac:
self.enable_mask_steps_gdac = range(self.mask_steps)
for mask_step in self.enable_mask_steps_gdac:
select_mask_array += make_pixel_mask(steps=self.mask_steps,
shift=mask_step)
for column in bits_set(
self.register.get_global_register_value("DisableColumnCnfg")):
logging.info('Deselect double column %d' % column)
select_mask_array[column, :] = 0
occupancy_best = 0.0
median_occupancy_last_step = 0.0
gdac_best = self.register_utils.get_gdac()
for gdac_scan_step, scan_parameter_value in enumerate(
scan_parameter_range):
self.register_utils.set_gdac(scan_parameter_value)
with self.readout(GDAC=scan_parameter_value,
reset_sram_fifo=True,
fill_buffer=True,
clear_buffer=True,
callback=self.handle_data):
scan_loop(self,
command=cal_lvl1_command,
repeat_command=self.n_injections_gdac,
mask_steps=self.mask_steps,
enable_mask_steps=self.enable_mask_steps_gdac,
enable_double_columns=None,
same_mask_for_all_dc=self.same_mask_for_all_dc,
eol_function=None,
digital_injection=False,
enable_shift_masks=self.enable_shift_masks,
disable_shift_masks=self.disable_shift_masks,
restore_shift_masks=True,
mask=None,
double_column_correction=self.pulser_dac_correction)
occupancy_array, _, _ = np.histogram2d(*convert_data_array(
data_array_from_data_iterable(self.fifo_readout.data),
filter_func=logical_and(is_fe_word, is_data_record),
converter_func=get_col_row_array_from_data_record_array),
bins=(80, 336),
range=[[1, 80], [1, 336]])
occ_array_sel_pixels = np.ma.array(
occupancy_array,
mask=np.logical_not(np.ma.make_mask(select_mask_array))
) # take only selected pixel into account by using the mask
occ_array_desel_pixels = np.ma.array(
occupancy_array, mask=np.ma.make_mask(select_mask_array)
) # take only de-selected pixel into account by using the inverted mask
median_occupancy = np.ma.median(occ_array_sel_pixels)
noise_occupancy = np.ma.median(occ_array_desel_pixels)
occupancy_almost_zero = np.allclose(median_occupancy, 0)
no_noise = np.allclose(noise_occupancy, 0)
if no_noise and not occupancy_almost_zero and abs(
median_occupancy - self.n_injections_gdac /
2) < abs(occupancy_best - self.n_injections_gdac / 2):
occupancy_best = median_occupancy
gdac_best = self.register_utils.get_gdac()
self.occ_array_sel_pixels_best = occ_array_sel_pixels.copy()
self.occ_array_desel_pixels_best = occ_array_desel_pixels.copy(
)
if self.plot_intermediate_steps:
plot_three_way(self.occ_array_sel_pixel.transpose(),
title="Occupancy (GDAC " +
str(scan_parameter_value) + ")",
x_axis_title='Occupancy',
filename=self.plots_filename,
maximum=self.n_injections_gdac)
if no_noise and not occupancy_almost_zero and median_occupancy >= median_occupancy_last_step and median_occupancy >= self.n_injections_gdac / 2:
break
if no_noise and not occupancy_almost_zero:
median_occupancy_last_step = median_occupancy
else:
median_occupancy_last_step = 0.0
self.register_utils.set_gdac(gdac_best, send_command=False)
median_occupancy = occupancy_best
self.gdac_best = self.register_utils.get_gdac()
if abs(median_occupancy -
self.n_injections_gdac / 2) > self.max_delta_threshold:
if np.all((((self.gdac_best & (1 << np.arange(
self.register.global_registers['Vthin_AltFine']
['bitlength'] + self.register.global_registers['Vthin_AltFine']
['bitlength'])))) > 0).astype(int) == 0):
if self.fail_on_warning:
raise RuntimeWarning(
'Selected GDAC bits reached minimum value')
else:
logging.warning('Selected GDAC bits reached minimum value')
else:
if self.fail_on_warning:
raise RuntimeWarning(
'Global threshold tuning failed. Delta threshold = %.2f > %.2f. Vthin_AltCoarse / Vthin_AltFine = %d / %d'
% (abs(median_occupancy - self.n_injections_gdac / 2),
self.max_delta_threshold,
self.register.get_global_register_value(
"Vthin_AltCoarse"),
self.register.get_global_register_value(
"Vthin_AltFine")))
else:
logging.warning(
'Global threshold tuning failed. Delta threshold = %.2f > %.2f. Vthin_AltCoarse / Vthin_AltFine = %d / %d',
abs(median_occupancy - self.n_injections_gdac / 2),
self.max_delta_threshold,
self.register.get_global_register_value(
"Vthin_AltCoarse"),
self.register.get_global_register_value(
"Vthin_AltFine"))
else:
logging.info(
'Tuned GDAC to Vthin_AltCoarse / Vthin_AltFine = %d / %d',
self.register.get_global_register_value("Vthin_AltCoarse"),
self.register.get_global_register_value("Vthin_AltFine"))