def analyze(self):
# set here because original value is restored after scan()
self.register.set_global_register_value("PrmpVbpf", self.feedback_best)
plot_tot(hist=self.tot_hist, title='ToT distribution after feedback tuning (PrmpVbpf %d)' % self.feedback_best, filename=self.plots_filename)
if self.close_plots:
self.plots_filename.close()
def analyze(self):
self.register.set_global_register_value("PrmpVbpf", self.feedback_best)
plot_tot(hist=self.tot_array,
title='ToT distribution after feedback tuning (PrmpVbpf %d)' %
self.scan_parameters.PrmpVbpf,
filename=self.plots_filename)
if self.close_plots:
self.plots_filename.close()
def analyze(self):
# set here because original value is restored after scan()
self.register.set_global_register_value("PrmpVbpf", self.feedback_best)
# write configuration to avoid high current states
commands = []
commands.extend(self.register.get_commands("ConfMode"))
commands.extend(
self.register.get_commands("WrRegister", name=["PrmpVbpf"]))
self.register_utils.send_commands(commands)
plot_tot(hist=self.tot_hist,
title='ToT distribution after feedback tuning (PrmpVbpf %d)' %
self.feedback_best,
filename=self.plots_filename)
if self.close_plots:
self.plots_filename.close()
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):
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 analyze(self):
self.register.set_global_register_value("PrmpVbpf", self.feedback_best)
plot_tot(hist=self.tot_array, title='ToT distribution after feedback tuning (PrmpVbpf %d)' % self.scan_parameters.PrmpVbpf, filename=self.plots_filename)
if self.close_plots:
self.plots_filename.close()
