def analyze_raw_data(in_file_h5, start, stop):
logging.info('Analyze raw data from word index ' + str(start) + ' to ' +
str(stop))
n_hits = 0
if not analysis_configuration['integrate']:
histograming.reset()
for iWord in range(start, stop, analysis_configuration['chunk_size']):
if stop - start <= analysis_configuration['chunk_size']:
stop_index = stop
else:
stop_index = iWord + analysis_configuration['chunk_size']
try:
logging.info('Take chunk from ' + str(iWord) + ' to ' +
str(stop_index))
raw_data = in_file_h5.root.raw_data.read(iWord, stop_index)
interpreter.interpret_raw_data(raw_data) # interpret the raw data
Nhits = interpreter.get_n_array_hits()
histograming.add_hits(hits[0:Nhits], Nhits)
n_hits += Nhits
except tb.exceptions.NoSuchNodeError:
logging.info('File in inconsistent state, omit hits')
except tb.exceptions.HDF5ExtError:
logging.info('File in inconsistent state, omit hits')
occupancy = np.zeros(
80 * 336 * histograming.get_n_parameters(), dtype=np.uint32
) # create linear array as it is created in histogram class
histograming.get_occupancy(occupancy)
occupancy_array = np.reshape(
a=occupancy.view(),
newshape=(80, 336, histograming.get_n_parameters()),
order='F') # make linear array to 3d array (col,row,parameter)
occupancy_array = np.swapaxes(occupancy_array, 0, 1)
occupancy_array_ma = np.ma.array(occupancy_array)[:, :, 0]
plotting.plot_fancy_occupancy(occupancy_array_ma, z_max='median')
# plotting.plot_occupancy(occupancy_array_ma)
error_counter_hist = np.zeros(16, dtype=np.uint32)
interpreter.get_error_counters(error_counter_hist)
plotting.plot_event_errors(error_counter_hist)
rel_bcid_hist = np.empty(16, dtype=np.uint32)
histograming.get_rel_bcid_hist(rel_bcid_hist)
plotting.plot_relative_bcid(rel_bcid_hist)
plt.pause(0.0001)
return n_hits
def analyze_data(scan_data_filenames, ignore_columns, fei4b=False):
logging.info("Analyzing and plotting results...")
output_h5_filename = local_configuration['output_data_filename'] + '.h5'
logging.info('Saving calibration in: %s' % output_h5_filename)
if local_configuration['create_plots'] or local_configuration['create_result_plots']:
output_pdf_filename = local_configuration['output_data_filename'] + '.pdf'
logging.info('Saving plots in: %s' % output_pdf_filename)
output_pdf = PdfPages(output_pdf_filename)
# define output data structures
mean_threshold_calibration = np.zeros(shape=(len(local_configuration['delays']),), dtype='<f8') # array to hold the analyzed data in ram
mean_threshold_rms_calibration = np.zeros_like(mean_threshold_calibration) # array to hold the analyzed data in ram
mean_noise_calibration = np.zeros_like(mean_threshold_calibration) # array to hold the analyzed data in ram
mean_noise_rms_calibration = np.zeros_like(mean_threshold_calibration) # array to hold the analyzed data in ram
threshold_calibration = np.zeros(shape=(80, 336, len(local_configuration['delays'])), dtype='<f8') # array to hold the analyzed data in ram
noise_calibration = np.zeros_like(threshold_calibration) # array to hold the analyzed data in ram
mean_threshold_calibration_1 = np.zeros_like(mean_threshold_calibration) # array to hold the analyzed data in ram
mean_threshold_rms_calibration_1 = np.zeros_like(mean_threshold_calibration) # array to hold the analyzed data in ram
threshold_calibration_1 = np.zeros_like(threshold_calibration) # array to hold the analyzed data in ram
mean_threshold_calibration_2 = np.zeros_like(mean_threshold_calibration) # array to hold the analyzed data in ram
mean_threshold_rms_calibration_2 = np.zeros_like(mean_threshold_calibration) # array to hold the analyzed data in ram
threshold_calibration_2 = np.zeros_like(threshold_calibration) # array to hold the analyzed data in ram
# initialize progress bar
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', analysis_utils.ETA()], maxval=len(local_configuration['delays']), term_width=80)
progress_bar.start()
# loop over all delay values and analyze the corresponding data
for delay_index, delay_value in enumerate(local_configuration['delays']):
# interpret the raw data from the actual delay value
raw_data_file = scan_data_filenames[delay_value]
analyzed_data_file = raw_data_file[:-3] + '_interpreted.h5'
analyze(raw_data_file=raw_data_file, analyzed_data_file=analyzed_data_file, fei4b=fei4b)
scan_parameters = None
with tb.openFile(analyzed_data_file, mode="r") as in_file_h5:
# mask the not scanned columns for analysis and plotting
mask = np.logical_or(mask_columns(pixel_array=in_file_h5.root.HistThresholdFitted[:], ignore_columns=ignore_columns), mask_pixel(steps=3, shift=0).T) == 0
occupancy_masked = mask_columns(pixel_array=in_file_h5.root.HistOcc[:], ignore_columns=ignore_columns)
thresholds_masked = np.ma.masked_array(in_file_h5.root.HistThresholdFitted[:], mask)
noise_masked = np.ma.masked_array(in_file_h5.root.HistNoiseFitted[:], mask)
# plot the threshold distribution and the s curves
if local_configuration['create_plots']:
plotting.plotThreeWay(hist=thresholds_masked * 55., title='Threshold Fitted for delay = ' + str(delay_value), x_axis_title='threshold [e]', filename=output_pdf)
plotting.plot_relative_bcid(hist=in_file_h5.root.HistRelBcid[0:16], title='Relative BCID (former LVL1ID) for delay = ' + str(delay_value), filename=output_pdf)
plotting.plot_event_errors(hist=in_file_h5.root.HistErrorCounter[:], title='Event status for delay = ' + str(delay_value), filename=output_pdf)
meta_data_array = in_file_h5.root.meta_data[:]
parameter_settings = analysis_utils.get_scan_parameter(meta_data_array=meta_data_array)
scan_parameters = parameter_settings['PlsrDAC']
if local_configuration['create_plots']:
plotting.plot_scurves(occupancy_hist=occupancy_masked, title='S-Curves, delay ' + str(delay_value), scan_parameters=scan_parameters, scan_parameter_name='PlsrDAC', filename=output_pdf)
# fill the calibration data arrays
mean_threshold_calibration[delay_index] = np.ma.mean(thresholds_masked)
mean_threshold_rms_calibration[delay_index] = np.ma.std(thresholds_masked)
threshold_calibration[:, :, delay_index] = thresholds_masked.T
mean_noise_calibration[delay_index] = np.ma.mean(noise_masked)
mean_noise_rms_calibration[delay_index] = np.ma.std(noise_masked)
noise_calibration[:, :, delay_index] = noise_masked.T
# if activated analyze also the trigger seperately
if local_configuration['analysis_two_trigger']:
with tb.openFile(analyzed_data_file[:-3] + '_analyzed_1.h5', mode="r") as in_file_1_h5:
with tb.openFile(analyzed_data_file[:-3] + '_analyzed_2.h5', mode="r") as in_file_2_h5:
# mask the not scanned columns for analysis and plotting
try:
occupancy_masked_1 = occupancy_masked = mask_columns(pixel_array=in_file_1_h5.root.HistOcc[:], ignore_columns=ignore_columns)
thresholds_masked_1 = np.ma.masked_array(in_file_1_h5.root.HistThresholdFitted[:], mask)
rel_bcid_1 = in_file_1_h5.root.HistRelBcid[0:16]
except tb.exceptions.NoSuchNodeError:
occupancy_masked_1 = np.zeros(shape=(336, 80, 2))
thresholds_masked_1 = np.zeros(shape=(336, 80))
rel_bcid_1 = np.zeros(shape=(16, ))
try:
occupancy_masked_2 = occupancy_masked = mask_columns(pixel_array=in_file_2_h5.root.HistOcc[:], ignore_columns=ignore_columns)
thresholds_masked_2 = np.ma.masked_array(in_file_2_h5.root.HistThresholdFitted[:], mask)
rel_bcid_2 = in_file_2_h5.root.HistRelBcid[0:16]
except tb.exceptions.NoSuchNodeError:
occupancy_masked_2 = np.zeros(shape=(336, 80, 2))
thresholds_masked_2 = np.zeros(shape=(336, 80))
rel_bcid_2 = np.zeros(shape=(16, ))
# plot the threshold distribution and the s curves
if local_configuration['create_plots']:
plotting.plotThreeWay(hist=thresholds_masked_1 * 55., title='Threshold Fitted for 1. trigger, delay ' + str(delay_value), x_axis_title='threshold [e]', filename=output_pdf)
plotting.plot_relative_bcid(hist=rel_bcid_1, title='Relative BCID (former LVL1ID) for 1. trigger, delay = ' + str(delay_value), filename=output_pdf)
plotting.plotThreeWay(hist=thresholds_masked_2 * 55., title='Threshold Fitted for 2. trigger, delay ' + str(delay_value), x_axis_title='threshold [e]', filename=output_pdf)
plotting.plot_relative_bcid(hist=rel_bcid_2, title='Relative BCID (former LVL1ID) for 2. trigger, delay = ' + str(delay_value), filename=output_pdf)
if local_configuration['create_plots']:
plotting.plot_scurves(occupancy_hist=occupancy_masked_1, title='S-Curves 1. trigger, delay ' + str(delay_value), scan_parameters=scan_parameters, scan_parameter_name='PlsrDAC', filename=output_pdf)
plotting.plot_scurves(occupancy_hist=occupancy_masked_2, title='S-Curves 2. trigger, delay ' + str(delay_value), scan_parameters=scan_parameters, scan_parameter_name='PlsrDAC', filename=output_pdf)
# fill the calibration data arrays
mean_threshold_calibration_1[delay_index] = np.ma.mean(thresholds_masked_1)
mean_threshold_rms_calibration_1[delay_index] = np.ma.std(thresholds_masked_1)
threshold_calibration_1[:, :, delay_index] = thresholds_masked_1.T
mean_threshold_calibration_2[delay_index] = np.ma.mean(thresholds_masked_2)
mean_threshold_rms_calibration_2[delay_index] = np.ma.std(thresholds_masked_2)
threshold_calibration_2[:, :, delay_index] = thresholds_masked_2.T
progress_bar.update(delay_index)
progress_bar.finish()
# plot the parameter against delay plots
if local_configuration['create_result_plots']:
plotting.plot_scatter(x=local_configuration['delays'], y=mean_threshold_calibration * 55., title='Threshold as a function of the delay', x_label='delay [BCID]', y_label='Mean threshold [e]', log_x=False, filename=output_pdf)
plotting.plot_scatter(x=local_configuration['delays'], y=mean_threshold_calibration * 55., title='Threshold as a function of the delay', x_label='delay [BCID]', y_label='Mean threshold [e]', log_x=True, filename=output_pdf)
plotting.plot_scatter(x=local_configuration['delays'], y=mean_threshold_rms_calibration * 55., title='Threshold as a function of the delay', x_label='delay [BCID]', y_label='Threshold RMS [e]', log_x=False, filename=output_pdf)
plotting.plot_scatter(x=local_configuration['delays'], y=mean_threshold_rms_calibration * 55., title='Threshold as a function of the delay', x_label='delay [BCID]', y_label='Threshold RMS [e]', log_x=True, filename=output_pdf)
if local_configuration['analysis_two_trigger']:
plotting.plot_scatter(x=local_configuration['delays'], y=mean_threshold_calibration_1 * 55., title='Threshold as a function of the delay, 1. trigger', x_label='delay [BCID]', y_label='Mean threshold [e]', log_x=False, filename=output_pdf)
plotting.plot_scatter(x=local_configuration['delays'], y=mean_threshold_calibration_1 * 55., title='Threshold as a function of the delay, 1. trigger', x_label='delay [BCID]', y_label='Mean threshold [e]', log_x=True, filename=output_pdf)
plotting.plot_scatter(x=local_configuration['delays'], y=mean_threshold_rms_calibration_1 * 55., title='Threshold as a function of the delay, 1. trigger', x_label='delay [BCID]', y_label='Threshold RMS [e]', log_x=False, filename=output_pdf)
plotting.plot_scatter(x=local_configuration['delays'], y=mean_threshold_rms_calibration_1 * 55., title='Threshold as a function of the delay, 1. trigger', x_label='delay [BCID]', y_label='Threshold RMS [e]', log_x=True, filename=output_pdf)
plotting.plot_scatter(x=local_configuration['delays'], y=mean_threshold_calibration_2 * 55., title='Threshold as a function of the delay, 2. trigger', x_label='delay [BCID]', y_label='Mean threshold [e]', log_x=False, filename=output_pdf)
plotting.plot_scatter(x=local_configuration['delays'], y=mean_threshold_calibration_2 * 55., title='Threshold as a function of the delay, 2. trigger', x_label='delay [BCID]', y_label='Mean threshold [e]', log_x=True, filename=output_pdf)
plotting.plot_scatter(x=local_configuration['delays'], y=mean_threshold_rms_calibration_2 * 55., title='Threshold as a function of the delay, 2. trigger', x_label='delay [BCID]', y_label='Threshold RMS [e]', log_x=False, filename=output_pdf)
plotting.plot_scatter(x=local_configuration['delays'], y=mean_threshold_rms_calibration_2 * 55., title='Threshold as a function of the delay, 2. trigger', x_label='delay [BCID]', y_label='Threshold RMS [e]', log_x=True, filename=output_pdf)
plotting.plot_scatter(x=local_configuration['delays'], y=mean_noise_calibration * 55., title='Noise as a function of the delay', x_label='delay [BCID]', y_label='Mean noise [e]', log_x=False, filename=output_pdf)
plotting.plot_scatter(x=local_configuration['delays'], y=mean_noise_rms_calibration * 55., title='Noise as a function of the delay', x_label='delay [BCID]', y_label='Noise RMS [e]', log_x=False, filename=output_pdf)
if local_configuration['create_plots'] or local_configuration['create_result_plots']:
output_pdf.close()
# store the calibration data into a hdf5 file as an easy to read table and as an array for quick data access
with tb.openFile(output_h5_filename, mode="w") as out_file_h5:
store_calibration_data_as_array(out_file_h5, mean_threshold_calibration, mean_threshold_rms_calibration, threshold_calibration, mean_noise_calibration, mean_noise_rms_calibration, noise_calibration)
store_calibration_data_as_table(out_file_h5, mean_threshold_calibration, mean_threshold_rms_calibration, threshold_calibration, mean_noise_calibration, mean_noise_rms_calibration, noise_calibration)
def analyze_data(scan_data_filenames, ignore_columns, fei4b=False):
logging.info("Analyzing and plotting results...")
output_h5_filename = local_configuration['output_data_filename'] + '.h5'
logging.info('Saving calibration in: %s' % output_h5_filename)
if local_configuration['create_plots'] or local_configuration[
'create_result_plots']:
output_pdf_filename = local_configuration[
'output_data_filename'] + '.pdf'
logging.info('Saving plots in: %s' % output_pdf_filename)
output_pdf = PdfPages(output_pdf_filename)
# define output data structures
mean_threshold_calibration = np.zeros(
shape=(len(local_configuration['delays']), ),
dtype='<f8') # array to hold the analyzed data in ram
mean_threshold_rms_calibration = np.zeros_like(
mean_threshold_calibration) # array to hold the analyzed data in ram
mean_noise_calibration = np.zeros_like(
mean_threshold_calibration) # array to hold the analyzed data in ram
mean_noise_rms_calibration = np.zeros_like(
mean_threshold_calibration) # array to hold the analyzed data in ram
threshold_calibration = np.zeros(
shape=(80, 336, len(local_configuration['delays'])),
dtype='<f8') # array to hold the analyzed data in ram
noise_calibration = np.zeros_like(
threshold_calibration) # array to hold the analyzed data in ram
mean_threshold_calibration_1 = np.zeros_like(
mean_threshold_calibration) # array to hold the analyzed data in ram
mean_threshold_rms_calibration_1 = np.zeros_like(
mean_threshold_calibration) # array to hold the analyzed data in ram
threshold_calibration_1 = np.zeros_like(
threshold_calibration) # array to hold the analyzed data in ram
mean_threshold_calibration_2 = np.zeros_like(
mean_threshold_calibration) # array to hold the analyzed data in ram
mean_threshold_rms_calibration_2 = np.zeros_like(
mean_threshold_calibration) # array to hold the analyzed data in ram
threshold_calibration_2 = np.zeros_like(
threshold_calibration) # array to hold the analyzed data in ram
# initialize progress bar
progress_bar = progressbar.ProgressBar(widgets=[
'',
progressbar.Percentage(), ' ',
progressbar.Bar(marker='*', left='|', right='|'), ' ',
analysis_utils.ETA()
],
maxval=len(
local_configuration['delays']),
term_width=80)
progress_bar.start()
# loop over all delay values and analyze the corresponding data
for delay_index, delay_value in enumerate(local_configuration['delays']):
# interpret the raw data from the actual delay value
raw_data_file = scan_data_filenames[delay_value]
analyzed_data_file = raw_data_file[:-3] + '_interpreted.h5'
analyze(raw_data_file=raw_data_file,
analyzed_data_file=analyzed_data_file,
fei4b=fei4b)
scan_parameters = None
with tb.openFile(analyzed_data_file, mode="r") as in_file_h5:
# mask the not scanned columns for analysis and plotting
mask = np.logical_or(
mask_columns(
pixel_array=in_file_h5.root.HistThresholdFitted[:],
ignore_columns=ignore_columns),
mask_pixel(steps=3, shift=0).T) == 0
occupancy_masked = mask_columns(
pixel_array=in_file_h5.root.HistOcc[:],
ignore_columns=ignore_columns)
thresholds_masked = np.ma.masked_array(
in_file_h5.root.HistThresholdFitted[:], mask)
noise_masked = np.ma.masked_array(
in_file_h5.root.HistNoiseFitted[:], mask)
# plot the threshold distribution and the s curves
if local_configuration['create_plots']:
plotting.plotThreeWay(hist=thresholds_masked * 55.,
title='Threshold Fitted for delay = ' +
str(delay_value),
x_axis_title='threshold [e]',
filename=output_pdf)
plotting.plot_relative_bcid(
hist=in_file_h5.root.HistRelBcid[0:16],
title='Relative BCID (former LVL1ID) for delay = ' +
str(delay_value),
filename=output_pdf)
plotting.plot_event_errors(
hist=in_file_h5.root.HistErrorCounter[:],
title='Event status for delay = ' + str(delay_value),
filename=output_pdf)
meta_data_array = in_file_h5.root.meta_data[:]
parameter_settings = analysis_utils.get_scan_parameter(
meta_data_array=meta_data_array)
scan_parameters = parameter_settings['PlsrDAC']
if local_configuration['create_plots']:
plotting.plot_scurves(occupancy_hist=occupancy_masked,
title='S-Curves, delay ' +
str(delay_value),
scan_parameters=scan_parameters,
scan_parameter_name='PlsrDAC',
filename=output_pdf)
# fill the calibration data arrays
mean_threshold_calibration[delay_index] = np.ma.mean(
thresholds_masked)
mean_threshold_rms_calibration[delay_index] = np.ma.std(
thresholds_masked)
threshold_calibration[:, :, delay_index] = thresholds_masked.T
mean_noise_calibration[delay_index] = np.ma.mean(noise_masked)
mean_noise_rms_calibration[delay_index] = np.ma.std(noise_masked)
noise_calibration[:, :, delay_index] = noise_masked.T
# if activated analyze also the trigger seperately
if local_configuration['analysis_two_trigger']:
with tb.openFile(analyzed_data_file[:-3] + '_analyzed_1.h5',
mode="r") as in_file_1_h5:
with tb.openFile(analyzed_data_file[:-3] + '_analyzed_2.h5',
mode="r") as in_file_2_h5:
# mask the not scanned columns for analysis and plotting
try:
occupancy_masked_1 = occupancy_masked = mask_columns(
pixel_array=in_file_1_h5.root.HistOcc[:],
ignore_columns=ignore_columns)
thresholds_masked_1 = np.ma.masked_array(
in_file_1_h5.root.HistThresholdFitted[:], mask)
rel_bcid_1 = in_file_1_h5.root.HistRelBcid[0:16]
except tb.exceptions.NoSuchNodeError:
occupancy_masked_1 = np.zeros(shape=(336, 80, 2))
thresholds_masked_1 = np.zeros(shape=(336, 80))
rel_bcid_1 = np.zeros(shape=(16, ))
try:
occupancy_masked_2 = occupancy_masked = mask_columns(
pixel_array=in_file_2_h5.root.HistOcc[:],
ignore_columns=ignore_columns)
thresholds_masked_2 = np.ma.masked_array(
in_file_2_h5.root.HistThresholdFitted[:], mask)
rel_bcid_2 = in_file_2_h5.root.HistRelBcid[0:16]
except tb.exceptions.NoSuchNodeError:
occupancy_masked_2 = np.zeros(shape=(336, 80, 2))
thresholds_masked_2 = np.zeros(shape=(336, 80))
rel_bcid_2 = np.zeros(shape=(16, ))
# plot the threshold distribution and the s curves
if local_configuration['create_plots']:
plotting.plotThreeWay(
hist=thresholds_masked_1 * 55.,
title='Threshold Fitted for 1. trigger, delay ' +
str(delay_value),
x_axis_title='threshold [e]',
filename=output_pdf)
plotting.plot_relative_bcid(
hist=rel_bcid_1,
title=
'Relative BCID (former LVL1ID) for 1. trigger, delay = '
+ str(delay_value),
filename=output_pdf)
plotting.plotThreeWay(
hist=thresholds_masked_2 * 55.,
title='Threshold Fitted for 2. trigger, delay ' +
str(delay_value),
x_axis_title='threshold [e]',
filename=output_pdf)
plotting.plot_relative_bcid(
hist=rel_bcid_2,
title=
'Relative BCID (former LVL1ID) for 2. trigger, delay = '
+ str(delay_value),
filename=output_pdf)
if local_configuration['create_plots']:
plotting.plot_scurves(
occupancy_hist=occupancy_masked_1,
title='S-Curves 1. trigger, delay ' +
str(delay_value),
scan_parameters=scan_parameters,
scan_parameter_name='PlsrDAC',
filename=output_pdf)
plotting.plot_scurves(
occupancy_hist=occupancy_masked_2,
title='S-Curves 2. trigger, delay ' +
str(delay_value),
scan_parameters=scan_parameters,
scan_parameter_name='PlsrDAC',
filename=output_pdf)
# fill the calibration data arrays
mean_threshold_calibration_1[delay_index] = np.ma.mean(
thresholds_masked_1)
mean_threshold_rms_calibration_1[delay_index] = np.ma.std(
thresholds_masked_1)
threshold_calibration_1[:, :,
delay_index] = thresholds_masked_1.T
mean_threshold_calibration_2[delay_index] = np.ma.mean(
thresholds_masked_2)
mean_threshold_rms_calibration_2[delay_index] = np.ma.std(
thresholds_masked_2)
threshold_calibration_2[:, :,
delay_index] = thresholds_masked_2.T
progress_bar.update(delay_index)
progress_bar.finish()
# plot the parameter against delay plots
if local_configuration['create_result_plots']:
plotting.plot_scatter(x=local_configuration['delays'],
y=mean_threshold_calibration * 55.,
title='Threshold as a function of the delay',
x_label='delay [BCID]',
y_label='Mean threshold [e]',
log_x=False,
filename=output_pdf)
plotting.plot_scatter(x=local_configuration['delays'],
y=mean_threshold_calibration * 55.,
title='Threshold as a function of the delay',
x_label='delay [BCID]',
y_label='Mean threshold [e]',
log_x=True,
filename=output_pdf)
plotting.plot_scatter(x=local_configuration['delays'],
y=mean_threshold_rms_calibration * 55.,
title='Threshold as a function of the delay',
x_label='delay [BCID]',
y_label='Threshold RMS [e]',
log_x=False,
filename=output_pdf)
plotting.plot_scatter(x=local_configuration['delays'],
y=mean_threshold_rms_calibration * 55.,
title='Threshold as a function of the delay',
x_label='delay [BCID]',
y_label='Threshold RMS [e]',
log_x=True,
filename=output_pdf)
if local_configuration['analysis_two_trigger']:
plotting.plot_scatter(
x=local_configuration['delays'],
y=mean_threshold_calibration_1 * 55.,
title='Threshold as a function of the delay, 1. trigger',
x_label='delay [BCID]',
y_label='Mean threshold [e]',
log_x=False,
filename=output_pdf)
plotting.plot_scatter(
x=local_configuration['delays'],
y=mean_threshold_calibration_1 * 55.,
title='Threshold as a function of the delay, 1. trigger',
x_label='delay [BCID]',
y_label='Mean threshold [e]',
log_x=True,
filename=output_pdf)
plotting.plot_scatter(
x=local_configuration['delays'],
y=mean_threshold_rms_calibration_1 * 55.,
title='Threshold as a function of the delay, 1. trigger',
x_label='delay [BCID]',
y_label='Threshold RMS [e]',
log_x=False,
filename=output_pdf)
plotting.plot_scatter(
x=local_configuration['delays'],
y=mean_threshold_rms_calibration_1 * 55.,
title='Threshold as a function of the delay, 1. trigger',
x_label='delay [BCID]',
y_label='Threshold RMS [e]',
log_x=True,
filename=output_pdf)
plotting.plot_scatter(
x=local_configuration['delays'],
y=mean_threshold_calibration_2 * 55.,
title='Threshold as a function of the delay, 2. trigger',
x_label='delay [BCID]',
y_label='Mean threshold [e]',
log_x=False,
filename=output_pdf)
plotting.plot_scatter(
x=local_configuration['delays'],
y=mean_threshold_calibration_2 * 55.,
title='Threshold as a function of the delay, 2. trigger',
x_label='delay [BCID]',
y_label='Mean threshold [e]',
log_x=True,
filename=output_pdf)
plotting.plot_scatter(
x=local_configuration['delays'],
y=mean_threshold_rms_calibration_2 * 55.,
title='Threshold as a function of the delay, 2. trigger',
x_label='delay [BCID]',
y_label='Threshold RMS [e]',
log_x=False,
filename=output_pdf)
plotting.plot_scatter(
x=local_configuration['delays'],
y=mean_threshold_rms_calibration_2 * 55.,
title='Threshold as a function of the delay, 2. trigger',
x_label='delay [BCID]',
y_label='Threshold RMS [e]',
log_x=True,
filename=output_pdf)
plotting.plot_scatter(x=local_configuration['delays'],
y=mean_noise_calibration * 55.,
title='Noise as a function of the delay',
x_label='delay [BCID]',
y_label='Mean noise [e]',
log_x=False,
filename=output_pdf)
plotting.plot_scatter(x=local_configuration['delays'],
y=mean_noise_rms_calibration * 55.,
title='Noise as a function of the delay',
x_label='delay [BCID]',
y_label='Noise RMS [e]',
log_x=False,
filename=output_pdf)
if local_configuration['create_plots'] or local_configuration[
'create_result_plots']:
output_pdf.close()
# store the calibration data into a hdf5 file as an easy to read table and as an array for quick data access
with tb.openFile(output_h5_filename, mode="w") as out_file_h5:
store_calibration_data_as_array(
out_file_h5, mean_threshold_calibration,
mean_threshold_rms_calibration, threshold_calibration,
mean_noise_calibration, mean_noise_rms_calibration,
noise_calibration)
store_calibration_data_as_table(
out_file_h5, mean_threshold_calibration,
mean_threshold_rms_calibration, threshold_calibration,
mean_noise_calibration, mean_noise_rms_calibration,
noise_calibration)