def analyze(self):
self.register.set_global_register_value("Vthin_AltFine", self.last_good_threshold[self.increase_threshold])
self.register.set_pixel_register_value('TDAC', self.last_good_tdac[self.increase_threshold])
self.register.set_pixel_register_value('Enable', self.last_good_enable_mask[0]) # use enable mask from the lowest point to mask bad pixels
# write configuration to avaoid high current states
commands = []
commands.extend(self.register.get_commands("ConfMode"))
commands.extend(self.register.get_commands("WrRegister", name=["Vthin_AltFine"]))
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, name="TDAC"))
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, name="Enable"))
self.register_utils.send_commands(commands)
with AnalyzeRawData(raw_data_file=self.output_filename, create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_source_scan_hist = True
analyze_raw_data.interpreter.set_warning_output(False)
analyze_raw_data.interpret_word_table()
analyze_raw_data.interpreter.print_summary()
analyze_raw_data.plot_histograms()
plot_occupancy(self.last_occupancy_hist[self.increase_threshold].T, title='Noisy Pixels at Vthin_AltFine %d Step %d' % (self.last_reg_val[self.increase_threshold], self.last_step[self.increase_threshold]), filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.last_occupancy_hist[self.increase_threshold].T, filename=analyze_raw_data.output_pdf)
plot_occupancy(self.last_occupancy_mask[self.increase_threshold].T, title='Occupancy Mask at Vthin_AltFine %d Step %d' % (self.last_reg_val[self.increase_threshold], self.last_step[self.increase_threshold]), z_max=1, filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.last_occupancy_mask[self.increase_threshold].T, filename=analyze_raw_data.output_pdf)
plot_three_way(self.last_good_tdac[self.increase_threshold].T, title='TDAC at Vthin_AltFine %d Step %d' % (self.last_reg_val[self.increase_threshold], self.last_step[self.increase_threshold]), x_axis_title="TDAC", filename=analyze_raw_data.output_pdf, maximum=31, bins=32)
plot_occupancy(self.last_good_tdac[self.increase_threshold].T, title='TDAC at Vthin_AltFine %d Step %d' % (self.last_reg_val[self.increase_threshold], self.last_step[self.increase_threshold]), z_max=31, filename=analyze_raw_data.output_pdf)
plot_occupancy(self.last_good_enable_mask[self.increase_threshold].T, title='Intermediate Enable Mask at Vthin_AltFine %d Step %d' % (self.last_reg_val[self.increase_threshold], self.last_step[self.increase_threshold]), z_max=1, filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.last_good_enable_mask[self.increase_threshold].T, filename=analyze_raw_data.output_pdf)
plot_occupancy(self.last_good_enable_mask[0].T, title='Final Enable Mask at Vthin_AltFine %d Step %d' % (self.last_reg_val[0], self.last_step[0]), z_max=1, filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.last_good_enable_mask[0].T, filename=analyze_raw_data.output_pdf)
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename, create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_tot_hist = True
if self.enable_tdc:
analyze_raw_data.create_tdc_counter_hist = True # histogram all TDC words
analyze_raw_data.create_tdc_hist = True # histogram the hit TDC information
analyze_raw_data.interpreter.use_tdc_word(True) # align events at the TDC word
analyze_raw_data.interpret_word_table()
analyze_raw_data.plot_histograms()
analyze_raw_data.interpreter.print_summary()
with tb.open_file(analyze_raw_data._analyzed_data_file, 'r') as out_file_h5:
occ_hist = out_file_h5.root.HistOcc[:, :, 0].T
occ_mask = np.zeros(shape=occ_hist.shape, dtype=np.dtype('>u1'))
occ_mask[occ_hist > 0] = 1
plot_occupancy(occ_mask.T, title='Merged Pixels', z_max=1, filename=analyze_raw_data.output_pdf)
inv_occ_mask = invert_pixel_mask(occ_mask)
if self.overwrite_mask:
for mask in self.disable_for_mask:
self.register.set_pixel_register_value(mask, inv_occ_mask)
else:
for mask in self.disable_for_mask:
enable_mask = np.logical_and(inv_occ_mask, self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, enable_mask)
if self.overwrite_mask:
for mask in self.enable_for_mask:
self.register.set_pixel_register_value(mask, occ_mask)
else:
for mask in self.enable_for_mask:
disable_mask = np.logical_or(occ_mask, self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, disable_mask)
def analyze(self):
self.register.set_global_register_value("Vthin_AltFine", self.last_good_threshold + self.increase_threshold)
self.register.set_pixel_register_value('TDAC', self.last_good_tdac)
self.register.set_pixel_register_value('Enable', self.last_good_enable_mask)
# write configuration to avaoid high current states
commands = []
commands.extend(self.register.get_commands("ConfMode"))
commands.extend(self.register.get_commands("WrRegister", name=["Vthin_AltFine"]))
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, name="TDAC"))
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, name="Enable"))
self.register_utils.send_commands(commands)
with AnalyzeRawData(raw_data_file=self.output_filename, create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_source_scan_hist = True
analyze_raw_data.interpreter.set_warning_output(False)
analyze_raw_data.interpret_word_table()
analyze_raw_data.interpreter.print_summary()
analyze_raw_data.plot_histograms()
plot_occupancy(self.last_occupancy_hist.T, title='Noisy Pixels at Vthin_AltFine %d Step %d' % (self.last_reg_val, self.last_step), filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.last_occupancy_hist.T, filename=analyze_raw_data.output_pdf)
plot_occupancy(self.last_occupancy_mask.T, title='Occupancy Mask at Vthin_AltFine %d Step %d' % (self.last_reg_val, self.last_step), z_max=1, filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.last_occupancy_mask.T, filename=analyze_raw_data.output_pdf)
plot_three_way(self.last_tdac_distribution.T, title='TDAC at Vthin_AltFine %d Step %d' % (self.last_reg_val, self.last_step), x_axis_title="TDAC", filename=analyze_raw_data.output_pdf, maximum=31, bins=32)
plot_occupancy(self.last_tdac_distribution.T, title='TDAC at Vthin_AltFine %d Step %d' % (self.last_reg_val, self.last_step), z_max=31, filename=analyze_raw_data.output_pdf)
plot_occupancy(self.register.get_pixel_register_value('Enable').T, title='Enable Mask', z_max=1, filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.register.get_pixel_register_value('Enable').T, filename=analyze_raw_data.output_pdf)
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename,
create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_tot_hist = True
if self.enable_tdc:
analyze_raw_data.create_tdc_counter_hist = True # histogram all TDC words
analyze_raw_data.create_tdc_hist = True # histogram the hit TDC information
analyze_raw_data.interpreter.use_tdc_word(
True) # align events at the TDC word
analyze_raw_data.interpret_word_table()
analyze_raw_data.plot_histograms()
analyze_raw_data.interpreter.print_summary()
with tb.open_file(analyze_raw_data._analyzed_data_file,
'r') as out_file_h5:
occ_hist = out_file_h5.root.HistOcc[:, :, 0].T
occ_mask = np.zeros(shape=occ_hist.shape, dtype=np.dtype('>u1'))
occ_mask[occ_hist > 0] = 1
plot_occupancy(occ_mask.T,
title='Merged Pixels',
z_max=1,
filename=analyze_raw_data.output_pdf)
inv_occ_mask = invert_pixel_mask(occ_mask)
if self.overwrite_mask:
for mask in self.disable_for_mask:
self.register.set_pixel_register_value(mask, inv_occ_mask)
else:
for mask in self.disable_for_mask:
enable_mask = np.logical_and(
inv_occ_mask,
self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, enable_mask)
if self.overwrite_mask:
for mask in self.enable_for_mask:
self.register.set_pixel_register_value(mask, occ_mask)
else:
for mask in self.enable_for_mask:
disable_mask = np.logical_or(
occ_mask, self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, disable_mask)
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename,
create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_tot_hist = False
analyze_raw_data.create_fitted_threshold_hists = True
analyze_raw_data.create_threshold_mask = True
analyze_raw_data.n_injections = 100
analyze_raw_data.interpreter.set_warning_output(
False
) # so far the data structure in a threshold scan was always bad, too many warnings given
analyze_raw_data.interpret_word_table()
analyze_raw_data.interpreter.print_summary()
analyze_raw_data.plot_histograms()
thr_hist = analyze_raw_data.out_file_h5.root.HistThresholdFitted[:, :].T
xtalk_mask = np.zeros(shape=thr_hist.shape, dtype=np.dtype('>u1'))
xtalk_mask[thr_hist > 0.0] = 1
plot_occupancy(xtalk_mask.T,
title='Crosstalk',
z_max=1,
filename=analyze_raw_data.output_pdf)
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename,
create_pdf=True) as analyze_raw_data:
analyze_raw_data.interpreter.set_warning_output(False)
analyze_raw_data.create_source_scan_hist = True
analyze_raw_data.create_hit_table = False
analyze_raw_data.interpret_word_table()
analyze_raw_data.plot_histograms()
analyze_raw_data.interpreter.print_summary()
with tb.open_file(analyze_raw_data._analyzed_data_file,
'r') as out_file_h5:
occ_hist = out_file_h5.root.HistOcc[:, :, 0].T
self.occ_mask = np.zeros(shape=occ_hist.shape,
dtype=np.dtype('>u1'))
# noisy pixels are set to 1
self.occ_mask[occ_hist > self.abs_occ_limit] = 1
# make inverse
self.inv_occ_mask = invert_pixel_mask(self.occ_mask)
if self.overwrite_mask:
for mask in self.disable_for_mask:
self.register.set_pixel_register_value(
mask, self.inv_occ_mask)
else:
for mask in self.disable_for_mask:
enable_mask = np.logical_and(
self.inv_occ_mask,
self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, enable_mask)
if self.overwrite_mask:
for mask in self.enable_for_mask:
self.register.set_pixel_register_value(mask, self.occ_mask)
else:
for mask in self.enable_for_mask:
disable_mask = np.logical_or(
self.occ_mask,
self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, disable_mask)
plot_occupancy(self.occ_mask.T,
title='Noisy Pixels',
z_max=1,
filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.occ_mask.T,
z_max=1,
filename=analyze_raw_data.output_pdf)
for mask in self.disable_for_mask:
mask_name = self.register.pixel_registers[mask]['name']
plot_occupancy(self.register.get_pixel_register_value(mask).T,
title='%s Mask' % mask_name,
z_max=1,
filename=analyze_raw_data.output_pdf)
for mask in self.enable_for_mask:
mask_name = self.register.pixel_registers[mask]['name']
plot_occupancy(self.register.get_pixel_register_value(mask).T,
title='%s Mask' % mask_name,
z_max=1,
filename=analyze_raw_data.output_pdf)
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename,
create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_source_scan_hist = True
analyze_raw_data.interpreter.set_warning_output(False)
analyze_raw_data.create_tot_hist = False
analyze_raw_data.interpret_word_table()
analyze_raw_data.plot_histograms()
analyze_raw_data.interpreter.print_summary()
# occ_hist = make_occupancy_hist(*convert_data_array(data_array_from_data_dict_iterable(self.fifo_readout.data), filter_func=is_data_record, converter_func=get_col_row_array_from_data_record_array)).T
with tb.open_file(analyze_raw_data._analyzed_data_file,
'r') as out_file_h5:
occ_hist = out_file_h5.root.HistOcc[:, :, 0].T
self.occ_mask = np.zeros(shape=occ_hist.shape,
dtype=np.dtype('>u1'))
# noisy pixels are set to 1
self.occ_mask[occ_hist < self.n_injections] = 1
# make inverse
self.inv_occ_mask = invert_pixel_mask(self.occ_mask)
self.disable_for_mask = self.disable_for_mask
if self.overwrite_mask:
for mask in self.disable_for_mask:
self.register.set_pixel_register_value(
mask, self.inv_occ_mask)
else:
for mask in self.disable_for_mask:
enable_mask = np.logical_and(
self.inv_occ_mask,
self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, enable_mask)
self.enable_for_mask = self.enable_for_mask
if self.overwrite_mask:
for mask in self.enable_for_mask:
self.register.set_pixel_register_value(mask, self.occ_mask)
else:
for mask in self.enable_for_mask:
disable_mask = np.logical_or(
self.occ_mask,
self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, disable_mask)
plot_occupancy(self.occ_mask.T,
title='Stuck Pixels',
z_max=1,
filename=analyze_raw_data.output_pdf)
for mask in self.disable_for_mask:
mask_name = self.register.pixel_registers[mask]['name']
plot_occupancy(self.register.get_pixel_register_value(mask).T,
title='%s Mask' % mask_name,
z_max=1,
filename=analyze_raw_data.output_pdf)
for mask in self.enable_for_mask:
mask_name = self.register.pixel_registers[mask]['name']
plot_occupancy(self.register.get_pixel_register_value(mask).T,
title='%s Mask' % mask_name,
z_max=1,
filename=analyze_raw_data.output_pdf)
def analyze(self):
self.register.set_global_register_value("Vthin_AltFine",
self.last_good_threshold)
self.register.set_pixel_register_value('TDAC', self.last_good_tdac)
self.register.set_pixel_register_value('Enable',
self.last_good_enable_mask)
with AnalyzeRawData(raw_data_file=self.output_filename,
create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_source_scan_hist = True
analyze_raw_data.interpreter.set_warning_output(False)
analyze_raw_data.interpret_word_table()
analyze_raw_data.interpreter.print_summary()
analyze_raw_data.plot_histograms()
plot_occupancy(self.last_occupancy_hist.T,
title='Noisy Pixels at Vthin_AltFine %d Step %d' %
(self.last_reg_val, self.last_step),
filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.last_occupancy_hist.T,
filename=analyze_raw_data.output_pdf)
plot_occupancy(self.last_occupancy_mask.T,
title='Occupancy Mask at Vthin_AltFine %d Step %d' %
(self.last_reg_val, self.last_step),
z_max=1,
filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.last_occupancy_mask.T,
filename=analyze_raw_data.output_pdf)
plotThreeWay(self.last_tdac_distribution.T,
title='TDAC at Vthin_AltFine %d Step %d' %
(self.last_reg_val, self.last_step),
x_axis_title="TDAC",
filename=analyze_raw_data.output_pdf,
maximum=31,
bins=32)
plot_occupancy(self.last_tdac_distribution.T,
title='TDAC at Vthin_AltFine %d Step %d' %
(self.last_reg_val, self.last_step),
z_max=31,
filename=analyze_raw_data.output_pdf)
plot_occupancy(self.register.get_pixel_register_value('Enable').T,
title='Enable Mask',
z_max=1,
filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(
self.register.get_pixel_register_value('Enable').T,
filename=analyze_raw_data.output_pdf)
def analyze(self):
self.register.set_global_register_value("Vthin_AltFine", self.last_good_threshold + self.increase_threshold)
self.register.set_pixel_register_value('TDAC', self.last_good_tdac)
self.register.set_pixel_register_value('Enable', self.last_good_enable_mask)
with AnalyzeRawData(raw_data_file=self.output_filename, create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_source_scan_hist = True
analyze_raw_data.interpreter.set_warning_output(False)
analyze_raw_data.interpret_word_table()
analyze_raw_data.interpreter.print_summary()
analyze_raw_data.plot_histograms()
plot_occupancy(self.last_occupancy_hist.T, title='Noisy Pixels at Vthin_AltFine %d Step %d' % (self.last_reg_val, self.last_step), filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.last_occupancy_hist.T, filename=analyze_raw_data.output_pdf)
plot_occupancy(self.last_occupancy_mask.T, title='Occupancy Mask at Vthin_AltFine %d Step %d' % (self.last_reg_val, self.last_step), z_max=1, filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.last_occupancy_mask.T, filename=analyze_raw_data.output_pdf)
plot_three_way(self.last_tdac_distribution.T, title='TDAC at Vthin_AltFine %d Step %d' % (self.last_reg_val, self.last_step), x_axis_title="TDAC", filename=analyze_raw_data.output_pdf, maximum=31, bins=32)
plot_occupancy(self.last_tdac_distribution.T, title='TDAC at Vthin_AltFine %d Step %d' % (self.last_reg_val, self.last_step), z_max=31, filename=analyze_raw_data.output_pdf)
plot_occupancy(self.register.get_pixel_register_value('Enable').T, title='Enable Mask', z_max=1, filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.register.get_pixel_register_value('Enable').T, filename=analyze_raw_data.output_pdf)
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename,
create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_tot_hist = True
if self.enable_tdc:
analyze_raw_data.create_tdc_counter_hist = True # histogram all TDC words
analyze_raw_data.create_tdc_hist = True # histogram the hit TDC information
analyze_raw_data.interpret_word_table()
analyze_raw_data.plot_histograms()
analyze_raw_data.interpreter.print_summary()
occ_hist = analyze_raw_data.out_file_h5.root.HistOcc[:, :, 0].T
occ_mask = np.zeros(shape=occ_hist.shape, dtype=np.dtype('>u1'))
occ_mask[occ_hist > 1] = 1
inv_occ_mask = invert_pixel_mask(occ_mask)
if self.overwrite_mask:
for mask in self.disable_for_mask:
self.register.set_pixel_register_value(mask, inv_occ_mask)
else:
for mask in self.disable_for_mask:
enable_mask = np.logical_and(
inv_occ_mask,
self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, enable_mask)
if self.overwrite_mask:
for mask in self.enable_for_mask:
self.register.set_pixel_register_value(mask, occ_mask)
else:
for mask in self.enable_for_mask:
disable_mask = np.logical_or(
occ_mask, self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, disable_mask)
plot_occupancy(occ_mask.T,
title='Merged Pixels',
z_max=1,
filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(occ_mask.T,
z_max=1,
filename=analyze_raw_data.output_pdf)
for mask in self.disable_for_mask:
mask_name = self.register.pixel_registers[mask]['name']
plot_occupancy(self.register.get_pixel_register_value(mask).T,
title='%s Mask' % mask_name,
z_max=1,
filename=analyze_raw_data.output_pdf)
for mask in self.enable_for_mask:
mask_name = self.register.pixel_registers[mask]['name']
plot_occupancy(self.register.get_pixel_register_value(mask).T,
title='%s Mask' % mask_name,
z_max=1,
filename=analyze_raw_data.output_pdf)
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename,
create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_source_scan_hist = True
analyze_raw_data.interpreter.set_warning_output(False)
analyze_raw_data.create_tot_hist = False
analyze_raw_data.interpret_word_table()
analyze_raw_data.plot_histograms()
analyze_raw_data.interpreter.print_summary()
occ_hist = analyze_raw_data.out_file_h5.root.HistOcc[:, :, 0].T
occ_mask = np.zeros(shape=occ_hist.shape, dtype=np.dtype('>u1'))
# noisy pixels are set to 1
occ_mask[occ_hist < self.n_injections] = 1
# make inverse
inv_occ_mask = invert_pixel_mask(occ_mask)
if self.overwrite_mask:
for mask in self.disable_for_mask:
self.register.set_pixel_register_value(mask, inv_occ_mask)
else:
for mask in self.disable_for_mask:
enable_mask = np.logical_and(
inv_occ_mask,
self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, enable_mask)
if self.overwrite_mask:
for mask in self.enable_for_mask:
self.register.set_pixel_register_value(mask, occ_mask)
else:
for mask in self.enable_for_mask:
disable_mask = np.logical_or(
occ_mask, self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, disable_mask)
plot_occupancy(occ_mask.T,
title='Stuck Pixels',
z_max=1,
filename=analyze_raw_data.output_pdf)
for mask in self.disable_for_mask:
mask_name = self.register.pixel_registers[mask]['name']
plot_occupancy(self.register.get_pixel_register_value(mask).T,
title='%s Mask' % mask_name,
z_max=1,
filename=analyze_raw_data.output_pdf)
for mask in self.enable_for_mask:
mask_name = self.register.pixel_registers[mask]['name']
plot_occupancy(self.register.get_pixel_register_value(mask).T,
title='%s Mask' % mask_name,
z_max=1,
filename=analyze_raw_data.output_pdf)
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename, create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_cluster_size_hist = False
analyze_raw_data.create_source_scan_hist = True
analyze_raw_data.create_cluster_tot_hist = False
analyze_raw_data.interpreter.set_warning_output(False)
analyze_raw_data.clusterizer.set_warning_output(False)
analyze_raw_data.interpret_word_table()
analyze_raw_data.interpreter.print_summary()
analyze_raw_data.plot_histograms()
with tb.open_file(analyze_raw_data._analyzed_data_file, 'r') as out_file_h5:
occ_hist = out_file_h5.root.HistOcc[:, :, 0].T
self.occ_mask = np.zeros(shape=occ_hist.shape, dtype=np.dtype('>u1'))
# n largest elements
n_largest_elements = np.sort(occ_hist[occ_hist > self.low_value])[-self.mask_high_count:]
# noisy pixels are set to 1
if n_largest_elements.shape[0] > 0:
self.occ_mask[occ_hist >= n_largest_elements[0]] = 1
# make inverse
self.inv_occ_mask = invert_pixel_mask(self.occ_mask)
if self.overwrite_mask:
for mask in self.disable_for_mask:
self.register.set_pixel_register_value(mask, self.inv_occ_mask)
else:
for mask in self.disable_for_mask:
enable_mask = np.logical_and(self.inv_occ_mask, self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, enable_mask)
if self.overwrite_mask:
for mask in self.enable_for_mask:
self.register.set_pixel_register_value(mask, self.occ_mask)
else:
for mask in self.enable_for_mask:
disable_mask = np.logical_or(self.occ_mask, self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, disable_mask)
plot_occupancy(self.occ_mask.T, title='Noisy Pixels', z_max=1, filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.occ_mask.T, z_max=1, filename=analyze_raw_data.output_pdf)
for mask in self.disable_for_mask:
mask_name = self.register.pixel_registers[mask]['name']
plot_occupancy(self.register.get_pixel_register_value(mask).T, title='%s Mask' % mask_name, z_max=1, filename=analyze_raw_data.output_pdf)
for mask in self.enable_for_mask:
mask_name = self.register.pixel_registers[mask]['name']
plot_occupancy(self.register.get_pixel_register_value(mask).T, title='%s Mask' % mask_name, z_max=1, filename=analyze_raw_data.output_pdf)
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename, create_pdf=True) as analyze_raw_data:
analyze_raw_data.interpreter.set_warning_output(False)
analyze_raw_data.create_source_scan_hist = True
analyze_raw_data.create_hit_table = False
analyze_raw_data.interpret_word_table()
analyze_raw_data.plot_histograms()
analyze_raw_data.interpreter.print_summary()
with tb.open_file(analyze_raw_data._analyzed_data_file, 'r') as out_file_h5:
occ_hist = out_file_h5.root.HistOcc[:, :, 0].T
self.occ_mask = np.zeros(shape=occ_hist.shape, dtype=np.dtype('>u1'))
# noisy pixels are set to 1
if self.trig_count == 0:
consecutive_lvl1 = (2 ** self.register.global_registers['Trig_Count']['bitlength'])
else:
consecutive_lvl1 = self.trig_count
self.occ_mask[occ_hist > self.occupancy_limit * self.n_triggers * consecutive_lvl1] = 1
# make inverse
self.inv_occ_mask = invert_pixel_mask(self.occ_mask)
if self.overwrite_mask:
for mask in self.disable_for_mask:
self.register.set_pixel_register_value(mask, self.inv_occ_mask)
else:
for mask in self.disable_for_mask:
enable_mask = np.logical_and(self.inv_occ_mask, self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, enable_mask)
if self.overwrite_mask:
for mask in self.enable_for_mask:
self.register.set_pixel_register_value(mask, self.occ_mask)
else:
for mask in self.enable_for_mask:
disable_mask = np.logical_or(self.occ_mask, self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, disable_mask)
plot_occupancy(self.occ_mask.T, title='Noisy Pixels', z_max=1, filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.occ_mask.T, z_max=1, filename=analyze_raw_data.output_pdf)
for mask in self.disable_for_mask:
mask_name = self.register.pixel_registers[mask]['name']
plot_occupancy(self.register.get_pixel_register_value(mask).T, title='%s Mask' % mask_name, z_max=1, filename=analyze_raw_data.output_pdf)
for mask in self.enable_for_mask:
mask_name = self.register.pixel_registers[mask]['name']
plot_occupancy(self.register.get_pixel_register_value(mask).T, title='%s Mask' % mask_name, z_max=1, filename=analyze_raw_data.output_pdf)
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename, create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_source_scan_hist = True
analyze_raw_data.interpreter.set_warning_output(False)
analyze_raw_data.create_tot_hist = False
analyze_raw_data.interpret_word_table()
analyze_raw_data.plot_histograms()
analyze_raw_data.interpreter.print_summary()
# occ_hist = make_occupancy_hist(*convert_data_array(data_array_from_data_dict_iterable(self.fifo_readout.data), filter_func=is_data_record, converter_func=get_col_row_array_from_data_record_array)).T
with tb.open_file(analyze_raw_data._analyzed_data_file, 'r') as out_file_h5:
occ_hist = out_file_h5.root.HistOcc[:, :, 0].T
self.occ_mask = np.zeros(shape=occ_hist.shape, dtype=np.dtype('>u1'))
# noisy pixels are set to 1
self.occ_mask[occ_hist < self.n_injections] = 1
# make inverse
self.inv_occ_mask = invert_pixel_mask(self.occ_mask)
self.disable_for_mask = self.disable_for_mask
if self.overwrite_mask:
for mask in self.disable_for_mask:
self.register.set_pixel_register_value(mask, self.inv_occ_mask)
else:
for mask in self.disable_for_mask:
enable_mask = np.logical_and(self.inv_occ_mask, self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, enable_mask)
self.enable_for_mask = self.enable_for_mask
if self.overwrite_mask:
for mask in self.enable_for_mask:
self.register.set_pixel_register_value(mask, self.occ_mask)
else:
for mask in self.enable_for_mask:
disable_mask = np.logical_or(self.occ_mask, self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, disable_mask)
plot_occupancy(self.occ_mask.T, title='Stuck Pixels', z_max=1, filename=analyze_raw_data.output_pdf)
for mask in self.disable_for_mask:
mask_name = self.register.pixel_registers[mask]['name']
plot_occupancy(self.register.get_pixel_register_value(mask).T, title='%s Mask' % mask_name, z_max=1, filename=analyze_raw_data.output_pdf)
for mask in self.enable_for_mask:
mask_name = self.register.pixel_registers[mask]['name']
plot_occupancy(self.register.get_pixel_register_value(mask).T, title='%s Mask' % mask_name, z_max=1, filename=analyze_raw_data.output_pdf)
def analyze_injected_charge(data_analyzed_file):
logging.info('Analyze the injected charge')
with tb.openFile(data_analyzed_file, mode="r") as in_file_h5:
occupancy = in_file_h5.root.HistOcc[:].T
gdacs = analysis_utils.get_scan_parameter(
in_file_h5.root.meta_data[:])['GDAC']
with PdfPages(data_analyzed_file[:-3] + '.pdf') as plot_file:
plotting.plot_scatter(
gdacs,
occupancy.sum(axis=(0, 1)),
title='Single pixel hit rate at different thresholds',
x_label='Threshold setting [GDAC]',
y_label='Single pixel hit rate',
log_x=True,
filename=plot_file)
if analysis_configuration['input_file_calibration']:
with tb.openFile(
analysis_configuration['input_file_calibration'],
mode="r"
) as in_file_calibration_h5: # read calibration file from calibrate_threshold_gdac scan
mean_threshold_calibration = in_file_calibration_h5.root.MeanThresholdCalibration[:]
threshold_calibration_array = in_file_calibration_h5.root.HistThresholdCalibration[:]
gdac_range_calibration = np.array(
in_file_calibration_h5.root.HistThresholdCalibration.
_v_attrs.scan_parameter_values)
gdac_range_source_scan = gdacs
# Select data that is within the given GDAC range, (min_gdac, max_gdac)
sel = np.where(
np.logical_and(
gdac_range_source_scan >=
analysis_configuration['min_gdac'],
gdac_range_source_scan <=
analysis_configuration['max_gdac']))[0]
gdac_range_source_scan = gdac_range_source_scan[sel]
occupancy = occupancy[:, :, sel]
sel = np.where(
np.logical_and(
gdac_range_calibration >=
analysis_configuration['min_gdac'],
gdac_range_calibration <=
analysis_configuration['max_gdac']))[0]
gdac_range_calibration = gdac_range_calibration[sel]
threshold_calibration_array = threshold_calibration_array[:, :,
sel]
logging.info(
'Analyzing source scan data with %d GDAC settings from %d to %d with minimum step sizes from %d to %d',
len(gdac_range_source_scan),
np.min(gdac_range_source_scan),
np.max(gdac_range_source_scan),
np.min(np.gradient(gdac_range_source_scan)),
np.max(np.gradient(gdac_range_source_scan)))
logging.info(
'Use calibration data with %d GDAC settings from %d to %d with minimum step sizes from %d to %d',
len(gdac_range_calibration),
np.min(gdac_range_calibration),
np.max(gdac_range_calibration),
np.min(np.gradient(gdac_range_calibration)),
np.max(np.gradient(gdac_range_calibration)))
# rate_normalization of the total hit number for each GDAC setting
rate_normalization = 1.
if analysis_configuration['normalize_rate']:
rate_normalization = analysis_utils.get_rate_normalization(
hit_file=hit_file,
cluster_file=hit_file,
parameter='GDAC',
reference=analysis_configuration[
'normalization_reference'],
plot=analysis_configuration['plot_normalization'])
# correcting the hit numbers for the different cluster sizes
correction_factors = 1.
if analysis_configuration['use_cluster_rate_correction']:
correction_h5 = tb.openFile(cluster_sizes_file,
mode="r")
cluster_size_histogram = correction_h5.root.AllHistClusterSize[:]
correction_factors = analysis_utils.get_hit_rate_correction(
gdacs=gdac_range_source_scan,
calibration_gdacs=gdac_range_source_scan,
cluster_size_histogram=cluster_size_histogram)
if analysis_configuration['plot_cluster_sizes']:
plot_cluster_sizes(
correction_h5,
in_file_calibration_h5,
gdac_range=gdac_range_source_scan)
pixel_thresholds = analysis_utils.get_pixel_thresholds_from_calibration_array(
gdacs=gdac_range_source_scan,
calibration_gdacs=gdac_range_calibration,
threshold_calibration_array=threshold_calibration_array
) # interpolates the threshold at the source scan GDAC setting from the calibration
pixel_hits = occupancy # create hit array with shape (col, row, ...)
pixel_hits = pixel_hits * correction_factors * rate_normalization
# choose region with pixels that have a sufficient occupancy but are not too hot
good_pixel = analysis_utils.select_good_pixel_region(
pixel_hits,
col_span=analysis_configuration['col_span'],
row_span=analysis_configuration['row_span'],
min_cut_threshold=analysis_configuration[
'min_cut_threshold'],
max_cut_threshold=analysis_configuration[
'max_cut_threshold'])
pixel_mask = ~np.ma.getmaskarray(good_pixel)
selected_pixel_hits = pixel_hits[
pixel_mask, :] # reduce the data to pixels that are in the good pixel region
selected_pixel_thresholds = pixel_thresholds[
pixel_mask, :] # reduce the data to pixels that are in the good pixel region
plotting.plot_occupancy(
good_pixel.T,
title='Selected pixel for analysis (' +
str(len(selected_pixel_hits)) + ')',
filename=plot_file)
# reshape to one dimension
x = selected_pixel_thresholds.flatten()
y = selected_pixel_hits.flatten()
# nothing should be NAN/INF, NAN/INF is not supported yet
if np.isfinite(x).shape != x.shape or np.isfinite(
y).shape != y.shape:
logging.warning(
'There are pixels with NaN or INF threshold or hit values, analysis will fail'
)
# calculated profile histogram
x_p, y_p, y_p_e = analysis_utils.get_profile_histogram(
x, y, n_bins=analysis_configuration['n_bins']
) # profile histogram data
# select only the data point where the calibration worked
selected_data = np.logical_and(
x_p > analysis_configuration['min_thr'] /
analysis_configuration['vcal_calibration'],
x_p < analysis_configuration['max_thr'] /
analysis_configuration['vcal_calibration'])
x_p = x_p[selected_data]
y_p = y_p[selected_data]
y_p_e = y_p_e[selected_data]
if len(y_p_e[y_p_e == 0]) != 0:
logging.warning(
'There are bins without any data, guessing the error bars'
)
y_p_e[y_p_e == 0] = np.amin(y_p_e[y_p_e != 0])
smoothed_data = analysis_utils.smooth_differentiation(
x_p,
y_p,
weigths=1 / y_p_e,
order=3,
smoothness=analysis_configuration['smoothness'],
derivation=0)
smoothed_data_diff = analysis_utils.smooth_differentiation(
x_p,
y_p,
weigths=1 / y_p_e,
order=3,
smoothness=analysis_configuration['smoothness'],
derivation=1)
with tb.openFile(data_analyzed_file[:-3] + '_result.h5',
mode="w") as out_file_h5:
result_1 = np.rec.array(np.column_stack(
(x_p, y_p, y_p_e)),
dtype=[('charge', float),
('count', float),
('count_error', float)])
result_2 = np.rec.array(np.column_stack(
(x_p, smoothed_data)),
dtype=[('charge', float),
('count', float)])
result_3 = np.rec.array(np.column_stack(
(x_p, -smoothed_data_diff)),
dtype=[('charge', float),
('count', float)])
out_1 = out_file_h5.create_table(
out_file_h5.root,
name='ProfileHistogram',
description=result_1.dtype,
title=
'Single pixel count rate combined with a profile histogram',
filters=tb.Filters(complib='blosc',
complevel=5,
fletcher32=False))
out_2 = out_file_h5.create_table(
out_file_h5.root,
name='ProfileHistogramSpline',
description=result_2.dtype,
title=
'Single pixel count rate combined with a profile histogram and spline smoothed',
filters=tb.Filters(complib='blosc',
complevel=5,
fletcher32=False))
out_3 = out_file_h5.create_table(
out_file_h5.root,
name='ChargeHistogram',
description=result_3.dtype,
title=
'Charge histogram with threshold method and per pixel calibration',
filters=tb.Filters(complib='blosc',
complevel=5,
fletcher32=False))
for key, value in analysis_configuration.iteritems():
out_1.attrs[key] = value
out_2.attrs[key] = value
out_3.attrs[key] = value
out_1.append(result_1)
out_2.append(result_2)
out_3.append(result_3)
plot_result(x_p, y_p, y_p_e, smoothed_data,
smoothed_data_diff)
# calculate and plot mean results
x_mean = analysis_utils.get_mean_threshold_from_calibration(
gdac_range_source_scan, mean_threshold_calibration)
y_mean = selected_pixel_hits.mean(axis=(0))
plotting.plot_scatter(
np.array(gdac_range_source_scan),
y_mean,
log_x=True,
plot_range=None,
title=
'Mean single pixel cluster rate at different thresholds',
x_label='threshold setting [GDAC]',
y_label='mean single pixel cluster rate',
filename=plot_file)
plotting.plot_scatter(
x_mean * analysis_configuration['vcal_calibration'],
y_mean,
plot_range=(analysis_configuration['min_thr'],
analysis_configuration['max_thr']),
title=
'Mean single pixel cluster rate at different thresholds',
x_label='mean threshold [e]',
y_label='mean single pixel cluster rate',
filename=plot_file)
if analysis_configuration['use_cluster_rate_correction']:
correction_h5.close()
def analyze_beam_spot(
scan_base,
combine_n_readouts=1000,
chunk_size=10000000,
plot_occupancy_hists=False,
output_pdf=None,
output_file=None,
):
""" Determines the mean x and y beam spot position as a function of time. Therefore the data of a fixed number of read outs are combined ('combine_n_readouts'). The occupancy is determined
for the given combined events and stored into a pdf file. At the end the beam x and y is plotted into a scatter plot with absolute positions in um.
Parameters
----------
scan_base: list of str
scan base names (e.g.: ['//data//SCC_50_fei4_self_trigger_scan_390', ]
combine_n_readouts: int
the number of read outs to combine (e.g. 1000)
max_chunk_size: int
the maximum chunk size used during read, if too big memory error occurs, if too small analysis takes longer
output_pdf: PdfPages
PdfPages file object, if none the plot is printed to screen
"""
time_stamp = []
x = []
y = []
for data_file in scan_base:
with tb.openFile(data_file + "_interpreted.h5", mode="r+") as in_hit_file_h5:
# get data and data pointer
meta_data_array = in_hit_file_h5.root.meta_data[:]
hit_table = in_hit_file_h5.root.Hits
# determine the event ranges to analyze (timestamp_start, start_event_number, stop_event_number)
parameter_ranges = np.column_stack(
(
analysis_utils.get_ranges_from_array(meta_data_array["timestamp_start"][::combine_n_readouts]),
analysis_utils.get_ranges_from_array(meta_data_array["event_number"][::combine_n_readouts]),
)
)
# create a event_numer index (important)
analysis_utils.index_event_number(hit_table)
# initialize the analysis and set settings
analyze_data = AnalyzeRawData()
analyze_data.create_tot_hist = False
analyze_data.create_bcid_hist = False
analyze_data.histograming.set_no_scan_parameter()
# variables for read speed up
index = 0 # index where to start the read out, 0 at the beginning, increased during looping
best_chunk_size = chunk_size
progress_bar = progressbar.ProgressBar(
widgets=[
"",
progressbar.Percentage(),
" ",
progressbar.Bar(marker="*", left="|", right="|"),
" ",
analysis_utils.ETA(),
],
maxval=hit_table.shape[0],
term_width=80,
)
progress_bar.start()
# loop over the selected events
for parameter_index, parameter_range in enumerate(parameter_ranges):
logging.debug(
"Analyze time stamp "
+ str(parameter_range[0])
+ " and data from events = ["
+ str(parameter_range[2])
+ ","
+ str(parameter_range[3])
+ "[ "
+ str(int(float(float(parameter_index) / float(len(parameter_ranges)) * 100.0)))
+ "%"
)
analyze_data.reset() # resets the data of the last analysis
# loop over the hits in the actual selected events with optimizations: determine best chunk size, start word index given
readout_hit_len = (
0
) # variable to calculate a optimal chunk size value from the number of hits for speed up
for hits, index in analysis_utils.data_aligned_at_events(
hit_table,
start_event_number=parameter_range[2],
stop_event_number=parameter_range[3],
start=index,
chunk_size=best_chunk_size,
):
analyze_data.analyze_hits(hits) # analyze the selected hits in chunks
readout_hit_len += hits.shape[0]
progress_bar.update(index)
best_chunk_size = (
int(1.5 * readout_hit_len) if int(1.05 * readout_hit_len) < chunk_size else chunk_size
) # to increase the readout speed, estimated the number of hits for one read instruction
# get and store results
occupancy_array = analyze_data.histograming.get_occupancy()
projection_x = np.sum(occupancy_array, axis=0).ravel()
projection_y = np.sum(occupancy_array, axis=1).ravel()
x.append(analysis_utils.get_mean_from_histogram(projection_x, bin_positions=range(0, 80)))
y.append(analysis_utils.get_mean_from_histogram(projection_y, bin_positions=range(0, 336)))
time_stamp.append(parameter_range[0])
if plot_occupancy_hists:
plotting.plot_occupancy(
occupancy_array[:, :, 0],
title="Occupancy for events between "
+ time.strftime("%H:%M:%S", time.localtime(parameter_range[0]))
+ " and "
+ time.strftime("%H:%M:%S", time.localtime(parameter_range[1])),
filename=output_pdf,
)
progress_bar.finish()
plotting.plot_scatter(
[i * 250 for i in x],
[i * 50 for i in y],
title="Mean beam position",
x_label="x [um]",
y_label="y [um]",
marker_style="-o",
filename=output_pdf,
)
if output_file:
with tb.openFile(output_file, mode="a") as out_file_h5:
rec_array = np.array(zip(time_stamp, x, y), dtype=[("time_stamp", float), ("x", float), ("y", float)])
try:
beam_spot_table = out_file_h5.createTable(
out_file_h5.root,
name="Beamspot",
description=rec_array,
title="Beam spot position",
filters=tb.Filters(complib="blosc", complevel=5, fletcher32=False),
)
beam_spot_table[:] = rec_array
except tb.exceptions.NodeError:
logging.warning(output_file + " has already a Beamspot note, do not overwrite existing.")
return time_stamp, x, y
def analyze_injected_charge(data_analyzed_file):
logging.info('Analyze the injected charge')
with tb.openFile(data_analyzed_file, mode="r") as in_file_h5:
occupancy = in_file_h5.root.HistOcc[:].T
gdacs = analysis_utils.get_scan_parameter(in_file_h5.root.meta_data[:])['GDAC']
with PdfPages(data_analyzed_file[:-3] + '.pdf') as plot_file:
plotting.plot_scatter(gdacs, occupancy.sum(axis=(0, 1)), title='Single pixel hit rate at different thresholds', x_label='Threshold setting [GDAC]', y_label='Single pixel hit rate', log_x=True, filename=plot_file)
if analysis_configuration['input_file_calibration']:
with tb.openFile(analysis_configuration['input_file_calibration'], mode="r") as in_file_calibration_h5: # read calibration file from calibrate_threshold_gdac scan
mean_threshold_calibration = in_file_calibration_h5.root.MeanThresholdCalibration[:]
threshold_calibration_array = in_file_calibration_h5.root.HistThresholdCalibration[:]
gdac_range_calibration = np.array(in_file_calibration_h5.root.HistThresholdCalibration._v_attrs.scan_parameter_values)
gdac_range_source_scan = gdacs
# Select data that is within the given GDAC range, (min_gdac, max_gdac)
sel = np.where(np.logical_and(gdac_range_source_scan >= analysis_configuration['min_gdac'], gdac_range_source_scan <= analysis_configuration['max_gdac']))[0]
gdac_range_source_scan = gdac_range_source_scan[sel]
occupancy = occupancy[:, :, sel]
sel = np.where(np.logical_and(gdac_range_calibration >= analysis_configuration['min_gdac'], gdac_range_calibration <= analysis_configuration['max_gdac']))[0]
gdac_range_calibration = gdac_range_calibration[sel]
threshold_calibration_array = threshold_calibration_array[:, :, sel]
logging.info('Analyzing source scan data with %d GDAC settings from %d to %d with minimum step sizes from %d to %d', len(gdac_range_source_scan), np.min(gdac_range_source_scan), np.max(gdac_range_source_scan), np.min(np.gradient(gdac_range_source_scan)), np.max(np.gradient(gdac_range_source_scan)))
logging.info('Use calibration data with %d GDAC settings from %d to %d with minimum step sizes from %d to %d', len(gdac_range_calibration), np.min(gdac_range_calibration), np.max(gdac_range_calibration), np.min(np.gradient(gdac_range_calibration)), np.max(np.gradient(gdac_range_calibration)))
# rate_normalization of the total hit number for each GDAC setting
rate_normalization = 1.
if analysis_configuration['normalize_rate']:
rate_normalization = analysis_utils.get_rate_normalization(hit_file=hit_file, cluster_file=hit_file, parameter='GDAC', reference=analysis_configuration['normalization_reference'], plot=analysis_configuration['plot_normalization'])
# correcting the hit numbers for the different cluster sizes
correction_factors = 1.
if analysis_configuration['use_cluster_rate_correction']:
correction_h5 = tb.openFile(cluster_sizes_file, mode="r")
cluster_size_histogram = correction_h5.root.AllHistClusterSize[:]
correction_factors = analysis_utils.get_hit_rate_correction(gdacs=gdac_range_source_scan, calibration_gdacs=gdac_range_source_scan, cluster_size_histogram=cluster_size_histogram)
if analysis_configuration['plot_cluster_sizes']:
plot_cluster_sizes(correction_h5, in_file_calibration_h5, gdac_range=gdac_range_source_scan)
pixel_thresholds = analysis_utils.get_pixel_thresholds_from_calibration_array(gdacs=gdac_range_source_scan, calibration_gdacs=gdac_range_calibration, threshold_calibration_array=threshold_calibration_array) # interpolates the threshold at the source scan GDAC setting from the calibration
pixel_hits = occupancy # create hit array with shape (col, row, ...)
pixel_hits = pixel_hits * correction_factors * rate_normalization
# choose region with pixels that have a sufficient occupancy but are not too hot
good_pixel = analysis_utils.select_good_pixel_region(pixel_hits, col_span=analysis_configuration['col_span'], row_span=analysis_configuration['row_span'], min_cut_threshold=analysis_configuration['min_cut_threshold'], max_cut_threshold=analysis_configuration['max_cut_threshold'])
pixel_mask = ~np.ma.getmaskarray(good_pixel)
selected_pixel_hits = pixel_hits[pixel_mask, :] # reduce the data to pixels that are in the good pixel region
selected_pixel_thresholds = pixel_thresholds[pixel_mask, :] # reduce the data to pixels that are in the good pixel region
plotting.plot_occupancy(good_pixel.T, title='Selected pixel for analysis (' + str(len(selected_pixel_hits)) + ')', filename=plot_file)
# reshape to one dimension
x = selected_pixel_thresholds.flatten()
y = selected_pixel_hits.flatten()
# nothing should be NAN/INF, NAN/INF is not supported yet
if np.isfinite(x).shape != x.shape or np.isfinite(y).shape != y.shape:
logging.warning('There are pixels with NaN or INF threshold or hit values, analysis will fail')
# calculated profile histogram
x_p, y_p, y_p_e = analysis_utils.get_profile_histogram(x, y, n_bins=analysis_configuration['n_bins']) # profile histogram data
# select only the data point where the calibration worked
selected_data = np.logical_and(x_p > analysis_configuration['min_thr'] / analysis_configuration['vcal_calibration'], x_p < analysis_configuration['max_thr'] / analysis_configuration['vcal_calibration'])
x_p = x_p[selected_data]
y_p = y_p[selected_data]
y_p_e = y_p_e[selected_data]
if len(y_p_e[y_p_e == 0]) != 0:
logging.warning('There are bins without any data, guessing the error bars')
y_p_e[y_p_e == 0] = np.amin(y_p_e[y_p_e != 0])
smoothed_data = analysis_utils.smooth_differentiation(x_p, y_p, weigths=1 / y_p_e, order=3, smoothness=analysis_configuration['smoothness'], derivation=0)
smoothed_data_diff = analysis_utils.smooth_differentiation(x_p, y_p, weigths=1 / y_p_e, order=3, smoothness=analysis_configuration['smoothness'], derivation=1)
with tb.openFile(data_analyzed_file[:-3] + '_result.h5', mode="w") as out_file_h5:
result_1 = np.rec.array(np.column_stack((x_p, y_p, y_p_e)), dtype=[('charge', float), ('count', float), ('count_error', float)])
result_2 = np.rec.array(np.column_stack((x_p, smoothed_data)), dtype=[('charge', float), ('count', float)])
result_3 = np.rec.array(np.column_stack((x_p, -smoothed_data_diff)), dtype=[('charge', float), ('count', float)])
out_1 = out_file_h5.create_table(out_file_h5.root, name='ProfileHistogram', description=result_1.dtype, title='Single pixel count rate combined with a profile histogram', filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
out_2 = out_file_h5.create_table(out_file_h5.root, name='ProfileHistogramSpline', description=result_2.dtype, title='Single pixel count rate combined with a profile histogram and spline smoothed', filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
out_3 = out_file_h5.create_table(out_file_h5.root, name='ChargeHistogram', description=result_3.dtype, title='Charge histogram with threshold method and per pixel calibration', filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
for key, value in analysis_configuration.iteritems():
out_1.attrs[key] = value
out_2.attrs[key] = value
out_3.attrs[key] = value
out_1.append(result_1)
out_2.append(result_2)
out_3.append(result_3)
plot_result(x_p, y_p, y_p_e, smoothed_data, smoothed_data_diff)
# calculate and plot mean results
x_mean = analysis_utils.get_mean_threshold_from_calibration(gdac_range_source_scan, mean_threshold_calibration)
y_mean = selected_pixel_hits.mean(axis=(0))
plotting.plot_scatter(np.array(gdac_range_source_scan), y_mean, log_x=True, plot_range=None, title='Mean single pixel cluster rate at different thresholds', x_label='threshold setting [GDAC]', y_label='mean single pixel cluster rate', filename=plot_file)
plotting.plot_scatter(x_mean * analysis_configuration['vcal_calibration'], y_mean, plot_range=(analysis_configuration['min_thr'], analysis_configuration['max_thr']), title='Mean single pixel cluster rate at different thresholds', x_label='mean threshold [e]', y_label='mean single pixel cluster rate', filename=plot_file)
if analysis_configuration['use_cluster_rate_correction']:
correction_h5.close()
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename,
create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_cluster_size_hist = False
analyze_raw_data.create_source_scan_hist = True
analyze_raw_data.create_cluster_tot_hist = False
analyze_raw_data.interpreter.set_warning_output(False)
analyze_raw_data.interpret_word_table()
analyze_raw_data.interpreter.print_summary()
analyze_raw_data.plot_histograms()
occ_hist = analyze_raw_data.out_file_h5.root.HistOcc[:, :, 0].T
self.occ_mask = np.zeros(shape=occ_hist.shape,
dtype=np.dtype('>u1'))
# n largest elements
n_largest_elements = np.sort(
occ_hist[occ_hist > self.low_value])[-self.mask_high_count:]
# noisy pixels are set to 1
if n_largest_elements.shape[0] > 0:
self.occ_mask[occ_hist >= n_largest_elements[0]] = 1
# make inverse
self.inv_occ_mask = invert_pixel_mask(self.occ_mask)
if self.overwrite_mask:
for mask in self.disable_for_mask:
self.register.set_pixel_register_value(
mask, self.inv_occ_mask)
else:
for mask in self.disable_for_mask:
enable_mask = np.logical_and(
self.inv_occ_mask,
self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, enable_mask)
if self.overwrite_mask:
for mask in self.enable_for_mask:
self.register.set_pixel_register_value(mask, self.occ_mask)
else:
for mask in self.enable_for_mask:
disable_mask = np.logical_or(
self.occ_mask,
self.register.get_pixel_register_value(mask))
self.register.set_pixel_register_value(mask, disable_mask)
plot_occupancy(self.occ_mask.T,
title='Noisy Pixels',
z_max=1,
filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.occ_mask.T,
z_max=1,
filename=analyze_raw_data.output_pdf)
for mask in self.disable_for_mask:
mask_name = self.register.pixel_registers[mask]['name']
plot_occupancy(self.register.get_pixel_register_value(mask).T,
title='%s Mask' % mask_name,
z_max=1,
filename=analyze_raw_data.output_pdf)
for mask in self.enable_for_mask:
mask_name = self.register.pixel_registers[mask]['name']
plot_occupancy(self.register.get_pixel_register_value(mask).T,
title='%s Mask' % mask_name,
z_max=1,
filename=analyze_raw_data.output_pdf)
def analyze(self):
with AnalyzeRawData(raw_data_file=self.output_filename, create_pdf=True) as analyze_raw_data:
analyze_raw_data.interpreter.set_warning_output(False)
analyze_raw_data.create_source_scan_hist = True
analyze_raw_data.create_hit_table = False
analyze_raw_data.interpret_word_table()
analyze_raw_data.plot_histograms()
analyze_raw_data.interpreter.print_summary()
# get occupancy hist
occ_hist = analyze_raw_data.out_file_h5.root.HistOcc[:, :, 0].T
self.occ_mask = np.zeros(shape=occ_hist.shape, dtype=np.dtype('>u1'))
# noisy pixels are set to 1
self.occ_mask[occ_hist > self.abs_occ_limit] = 1
# make inverse
self.inv_occ_mask = invert_pixel_mask(self.occ_mask)
# generate masked occupancy hist
masked_occ_hist = occ_hist.copy()
masked_occ_hist[self.occ_mask == 1] = 0
if self.overwrite_mask:
for mask in self.disable_for_mask:
self.register.set_pixel_register_value(mask, self.inv_occ_mask)
else:
for mask in self.disable_for_mask:
enable_mask = self.register.get_pixel_register_value(mask)
new_enable_mask = np.logical_and(self.inv_occ_mask, enable_mask)
self.register.set_pixel_register_value(mask, new_enable_mask)
if self.overwrite_mask:
for mask in self.enable_for_mask:
self.register.set_pixel_register_value(mask, self.occ_mask)
else:
for mask in self.enable_for_mask:
disable_mask = self.register.get_pixel_register_value(mask)
new_disable_mask = np.logical_or(self.occ_mask, disable_mask)
self.register.set_pixel_register_value(mask, new_disable_mask)
plot_occupancy(self.occ_mask.T, title='Noisy Pixels', z_max=1, filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.occ_mask.T, z_max=1, filename=analyze_raw_data.output_pdf)
for mask in self.disable_for_mask:
mask_name = self.register.pixel_registers[mask]['name']
plot_occupancy(self.register.get_pixel_register_value(mask).T, title='%s Mask' % mask_name, z_max=1, filename=analyze_raw_data.output_pdf)
for mask in self.enable_for_mask:
mask_name = self.register.pixel_registers[mask]['name']
plot_occupancy(self.register.get_pixel_register_value(mask).T, title='%s Mask' % mask_name, z_max=1, filename=analyze_raw_data.output_pdf)
# adding Poisson statistics plots
fig = Figure()
FigureCanvas(fig)
ax = fig.add_subplot(111)
ax.set_title("Hit statistics")
hist, bin_edges = np.histogram(occ_hist, bins=np.arange(0, np.max(occ_hist) + 2, 1))
try:
_, idx = hist_quantiles(hist, [0.0, 0.9], return_indices=True)
except IndexError:
idx = [0, 1]
bins = np.arange(0, np.maximum(bin_edges[idx[1]], stats.poisson.ppf(0.9999, mu=self.occupancy_limit * self.n_triggers * self.consecutive_lvl1)) + 2, 1)
ax.hist(occ_hist.flatten(), bins=bins, align='left', alpha=0.5, label="Measured occupancy before masking noisy pixels")
ax.hist(masked_occ_hist.flatten(), bins=bins, align='left', alpha=0.5, label="Measured occupancy after masking noisy pixels")
ax.bar(x=bins[:-1], height=stats.poisson.pmf(k=bins[:-1], mu=self.occupancy_limit * self.n_triggers * self.consecutive_lvl1) * self.register.get_pixel_register_value("Enable").sum(), alpha=0.5, width=1.0, color="r", label="Expected occupancy (Poisson statistics)")
# ax.hist(stats.poisson.rvs(mu=self.occupancy_limit * self.n_triggers * self.consecutive_lvl1, size=self.register.get_pixel_register_value("Enable").sum()), bins=bins, align='left', alpha=0.5, label="Expected occupancy (Poisson statistics)")
ax.set_xlabel('#Hits')
ax.set_ylabel('#Pixels')
ax.legend()
analyze_raw_data.output_pdf.savefig(fig)
def analyze(self):
self.register.set_global_register_value("Vthin_AltFine", self.threshold[0])
self.register.set_pixel_register_value('TDAC', self.new_tdac[0])
self.register.set_pixel_register_value('Enable', self.new_enable_mask[0]) # use enable mask from the lowest point to mask bad pixels
# write configuration to avaoid high current states
commands = []
commands.extend(self.register.get_commands("ConfMode"))
commands.extend(self.register.get_commands("WrRegister", name=["Vthin_AltFine"]))
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, name="TDAC"))
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, name="Enable"))
self.register_utils.send_commands(commands)
with AnalyzeRawData(raw_data_file=self.output_filename, create_pdf=True) as analyze_raw_data:
analyze_raw_data.create_source_scan_hist = True
analyze_raw_data.interpreter.set_warning_output(False)
analyze_raw_data.interpret_word_table()
analyze_raw_data.interpreter.print_summary()
analyze_raw_data.plot_histograms()
last_step = None
for step in range(self.plot_n_steps, -1, -1):
if self.threshold[step] is not None:
plot_occupancy(self.occupancy_hist[step].T, title='Occupancy at Vthin_AltFine %d Step %d' % (self.threshold[step], self.tdac_step[step]), filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.occupancy_hist[step].T, filename=analyze_raw_data.output_pdf)
plot_occupancy(self.occupancy_mask[step].T, title='Noisy pixels at Vthin_AltFine %d Step %d' % (self.threshold[step], self.tdac_step[step]), z_max=1, filename=analyze_raw_data.output_pdf)
plot_fancy_occupancy(self.occupancy_mask[step].T, filename=analyze_raw_data.output_pdf)
plot_three_way(self.tdac[step].T, title='TDAC at Vthin_AltFine %d Step %d' % (self.threshold[step], self.tdac_step[step]), x_axis_title="TDAC", filename=analyze_raw_data.output_pdf, maximum=31, bins=32)
plot_occupancy(self.tdac[step].T, title='TDAC at Vthin_AltFine %d Step %d' % (self.threshold[step], self.tdac_step[step]), z_max=31, filename=analyze_raw_data.output_pdf)
plot_occupancy(self.enable_mask[step].T, title='Enable mask at Vthin_AltFine %d Step %d' % (self.threshold[step], self.tdac_step[step]), z_max=1, filename=analyze_raw_data.output_pdf)
# adding Poisson statistics plots
fig = Figure()
FigureCanvas(fig)
ax = fig.add_subplot(111)
ax.set_title("Hit statistics")
hist, bin_edges = np.histogram(self.occupancy_hist[step], bins=np.arange(0.0, np.max(self.occupancy_hist[step]) + 2, 1.0))
try:
_, idx = hist_quantiles(hist, [0.0, 0.9], return_indices=True)
except IndexError:
idx = [0, 1]
bins = np.arange(0, np.maximum(bin_edges[idx[1]], stats.poisson.ppf(0.9999, mu=self.occupancy_limit * self.n_triggers * self.consecutive_lvl1)) + 2, 1)
ax.hist(self.occupancy_hist[step].flatten(), bins=bins, align='left', alpha=0.5, label="Measured occupancy")
ax.bar(x=bins[:-1], height=stats.poisson.pmf(k=bins[:-1], mu=self.occupancy_limit * self.n_triggers * self.consecutive_lvl1) * self.enable_mask[step].sum(), alpha=0.5, width=1.0, color="r", label="Expected occupancy (Poisson statistics)")
# ax.hist(stats.poisson.rvs(mu=self.occupancy_limit * self.n_triggers * self.consecutive_lvl1, size=self.enable_mask[step].sum()), bins=bins, align='left', alpha=0.5, label="Expected occupancy (Poisson statistics)")
ax.set_xlabel('#Hits')
ax.set_ylabel('#Pixels')
ax.legend()
analyze_raw_data.output_pdf.savefig(fig)
last_step = step
if last_step is not None:
plot_three_way(self.new_tdac[last_step].T, title='Final TDAC after Vthin_AltFine %d Step %d' % (self.threshold[last_step], self.tdac_step[last_step]), x_axis_title="TDAC", filename=analyze_raw_data.output_pdf, maximum=31, bins=32)
plot_occupancy(self.new_tdac[last_step].T, title='Final TDAC after Vthin_AltFine %d Step %d' % (self.threshold[last_step], self.tdac_step[last_step]), z_max=31, filename=analyze_raw_data.output_pdf)
plot_occupancy(self.new_enable_mask[last_step].T, title='Final Enable mask after Vthin_AltFine %d Step %d' % (self.threshold[last_step], self.tdac_step[last_step]), z_max=1, filename=analyze_raw_data.output_pdf)
def analyze_beam_spot(scan_base,
combine_n_readouts=1000,
chunk_size=10000000,
plot_occupancy_hists=False,
output_pdf=None,
output_file=None):
''' Determines the mean x and y beam spot position as a function of time. Therefore the data of a fixed number of read outs are combined ('combine_n_readouts'). The occupancy is determined
for the given combined events and stored into a pdf file. At the end the beam x and y is plotted into a scatter plot with absolute positions in um.
Parameters
----------
scan_base: list of str
scan base names (e.g.: ['//data//SCC_50_fei4_self_trigger_scan_390', ]
combine_n_readouts: int
the number of read outs to combine (e.g. 1000)
max_chunk_size: int
the maximum chunk size used during read, if too big memory error occurs, if too small analysis takes longer
output_pdf: PdfPages
PdfPages file object, if none the plot is printed to screen
'''
time_stamp = []
x = []
y = []
for data_file in scan_base:
with tb.open_file(data_file + '_interpreted.h5',
mode="r+") as in_hit_file_h5:
# get data and data pointer
meta_data_array = in_hit_file_h5.root.meta_data[:]
hit_table = in_hit_file_h5.root.Hits
# determine the event ranges to analyze (timestamp_start, start_event_number, stop_event_number)
parameter_ranges = np.column_stack(
(analysis_utils.get_ranges_from_array(
meta_data_array['timestamp_start'][::combine_n_readouts]),
analysis_utils.get_ranges_from_array(
meta_data_array['event_number'][::combine_n_readouts])))
# create a event_numer index (important)
analysis_utils.index_event_number(hit_table)
# initialize the analysis and set settings
analyze_data = AnalyzeRawData()
analyze_data.create_tot_hist = False
analyze_data.create_bcid_hist = False
analyze_data.histogram.set_no_scan_parameter()
# variables for read speed up
index = 0 # index where to start the read out, 0 at the beginning, increased during looping
best_chunk_size = chunk_size
progress_bar = progressbar.ProgressBar(widgets=[
'',
progressbar.Percentage(), ' ',
progressbar.Bar(marker='*', left='|', right='|'), ' ',
progressbar.AdaptiveETA()
],
maxval=hit_table.shape[0],
term_width=80)
progress_bar.start()
# loop over the selected events
for parameter_index, parameter_range in enumerate(
parameter_ranges):
logging.debug('Analyze time stamp ' + str(parameter_range[0]) +
' and data from events = [' +
str(parameter_range[2]) + ',' +
str(parameter_range[3]) + '[ ' + str(
int(
float(
float(parameter_index) /
float(len(parameter_ranges)) *
100.0))) + '%')
analyze_data.reset() # resets the data of the last analysis
# loop over the hits in the actual selected events with optimizations: determine best chunk size, start word index given
readout_hit_len = 0 # variable to calculate a optimal chunk size value from the number of hits for speed up
for hits, index in analysis_utils.data_aligned_at_events(
hit_table,
start_event_number=parameter_range[2],
stop_event_number=parameter_range[3],
start_index=index,
chunk_size=best_chunk_size):
analyze_data.analyze_hits(
hits) # analyze the selected hits in chunks
readout_hit_len += hits.shape[0]
progress_bar.update(index)
best_chunk_size = int(1.5 * readout_hit_len) if int(
1.05 * readout_hit_len
) < chunk_size else chunk_size # to increase the readout speed, estimated the number of hits for one read instruction
# get and store results
occupancy_array = analyze_data.histogram.get_occupancy()
projection_x = np.sum(occupancy_array, axis=0).ravel()
projection_y = np.sum(occupancy_array, axis=1).ravel()
x.append(
analysis_utils.get_mean_from_histogram(projection_x,
bin_positions=range(
0, 80)))
y.append(
analysis_utils.get_mean_from_histogram(projection_y,
bin_positions=range(
0, 336)))
time_stamp.append(parameter_range[0])
if plot_occupancy_hists:
plotting.plot_occupancy(
occupancy_array[:, :, 0],
title='Occupancy for events between ' + time.strftime(
'%H:%M:%S', time.localtime(parameter_range[0])) +
' and ' + time.strftime(
'%H:%M:%S', time.localtime(parameter_range[1])),
filename=output_pdf)
progress_bar.finish()
plotting.plot_scatter([i * 250 for i in x], [i * 50 for i in y],
title='Mean beam position',
x_label='x [um]',
y_label='y [um]',
marker_style='-o',
filename=output_pdf)
if output_file:
with tb.open_file(output_file, mode="a") as out_file_h5:
rec_array = np.array(zip(time_stamp, x, y),
dtype=[('time_stamp', float), ('x', float),
('y', float)])
try:
beam_spot_table = out_file_h5.create_table(
out_file_h5.root,
name='Beamspot',
description=rec_array,
title='Beam spot position',
filters=tb.Filters(complib='blosc',
complevel=5,
fletcher32=False))
beam_spot_table[:] = rec_array
except tb.exceptions.NodeError:
logging.warning(
output_file +
' has already a Beamspot note, do not overwrite existing.')
return time_stamp, x, y
def analyze_injected_charge(data_analyzed_file):
logging.info('Analyze the injected charge')
with tb.openFile(data_analyzed_file, mode="r") as in_file_h5:
occupancy = in_file_h5.root.HistOcc[:]
gdacs = analysis_utils.get_scan_parameter(
in_file_h5.root.meta_data[:])['GDAC']
with tb.openFile(
analysis_configuration['input_file_calibration'], mode="r"
) as in_file_calibration_h5: # read calibration file from calibrate_threshold_gdac scan
mean_threshold_calibration = in_file_calibration_h5.root.MeanThresholdCalibration[:]
threshold_calibration_array = in_file_calibration_h5.root.HistThresholdCalibration[:]
gdac_range_calibration = mean_threshold_calibration['gdac']
gdac_range_source_scan = gdacs
logging.info(
'Analyzing source scan data with %d GDAC settings from %d to %d with minimum step sizes from %d to %d'
% (len(gdac_range_source_scan), np.min(gdac_range_source_scan),
np.max(gdac_range_source_scan),
np.min(np.gradient(gdac_range_source_scan)),
np.max(np.gradient(gdac_range_source_scan))))
logging.info(
'Use calibration data with %d GDAC settings from %d to %d with minimum step sizes from %d to %d'
% (len(gdac_range_calibration), np.min(gdac_range_calibration),
np.max(gdac_range_calibration),
np.min(np.gradient(gdac_range_calibration)),
np.max(np.gradient(gdac_range_calibration))))
# rate_normalization of the total hit number for each GDAC setting
rate_normalization = 1.
if analysis_configuration['normalize_rate']:
rate_normalization = analysis_utils.get_rate_normalization(
hit_file=hit_file,
cluster_file=hit_file,
parameter='GDAC',
reference=analysis_configuration[
'normalization_reference'],
plot=analysis_configuration['plot_normalization'])
# correcting the hit numbers for the different cluster sizes
correction_factors = 1.
if analysis_configuration['use_cluster_rate_correction']:
correction_h5 = tb.openFile(cluster_sizes_file, mode="r")
cluster_size_histogram = correction_h5.root.AllHistClusterSize[:]
correction_factors = analysis_utils.get_hit_rate_correction(
gdacs=gdac_range_source_scan,
calibration_gdacs=gdac_range_source_scan,
cluster_size_histogram=cluster_size_histogram)
if analysis_configuration['plot_cluster_sizes']:
plot_cluster_sizes(correction_h5,
in_file_calibration_h5,
gdac_range=gdac_range_source_scan)
print correction_factors
pixel_thresholds = analysis_utils.get_pixel_thresholds_from_calibration_array(
gdacs=gdac_range_source_scan,
calibration_gdacs=gdac_range_calibration,
threshold_calibration_array=threshold_calibration_array
) # interpolates the threshold at the source scan GDAC setting from the calibration
pixel_hits = np.swapaxes(
occupancy, 0, 1) # create hit array with shape (col, row, ...)
pixel_hits = pixel_hits * correction_factors * rate_normalization
# choose region with pixels that have a sufficient occupancy but are not too hot
good_pixel = analysis_utils.select_good_pixel_region(
pixel_hits,
col_span=analysis_configuration['col_span'],
row_span=analysis_configuration['row_span'],
min_cut_threshold=analysis_configuration['min_cut_threshold'],
max_cut_threshold=analysis_configuration['max_cut_threshold'])
pixel_mask = ~np.ma.getmaskarray(good_pixel)
selected_pixel_hits = pixel_hits[
pixel_mask, :] # reduce the data to pixels that are in the good pixel region
selected_pixel_thresholds = pixel_thresholds[
pixel_mask, :] # reduce the data to pixels that are in the good pixel region
plotting.plot_occupancy(good_pixel.T,
title='Select ' +
str(len(selected_pixel_hits)) +
' pixels for analysis')
# reshape to one dimension
x = selected_pixel_thresholds.flatten()
y = selected_pixel_hits.flatten()
#nothing should be NAN, NAN is not supported yet
if np.isfinite(x).shape != x.shape or np.isfinite(
y).shape != y.shape:
logging.warning(
'There are pixels with NaN or INF threshold or hit values, analysis will fail'
)
# calculated profile histogram
x_p, y_p, y_p_e = analysis_utils.get_profile_histogram(
x, y, n_bins=analysis_configuration['n_bins']
) # profile histogram data
# select only the data point where the calibration worked
selected_data = np.logical_and(
x_p > analysis_configuration['min_thr'] /
analysis_configuration['vcal_calibration'],
x_p < analysis_configuration['max_thr'] /
analysis_configuration['vcal_calibration'])
x_p = x_p[selected_data]
y_p = y_p[selected_data]
y_p_e = y_p_e[selected_data]
plot_result(x_p, y_p, y_p_e)
# calculate and plot mean results
x_mean = analysis_utils.get_mean_threshold_from_calibration(
gdac_range_source_scan, mean_threshold_calibration)
y_mean = selected_pixel_hits.mean(axis=(0))
plotting.plot_scatter(
np.array(gdac_range_source_scan),
y_mean,
log_x=True,
plot_range=None,
title='Mean single pixel cluster rate at different thresholds',
x_label='threshold setting [GDAC]',
y_label='mean single pixel cluster rate')
plotting.plot_scatter(
x_mean * analysis_configuration['vcal_calibration'],
y_mean,
plot_range=(analysis_configuration['min_thr'],
analysis_configuration['max_thr']),
title='Mean single pixel cluster rate at different thresholds',
x_label='mean threshold [e]',
y_label='mean single pixel cluster rate')
if analysis_configuration['use_cluster_rate_correction']:
correction_h5.close()
