def select_trigger_hits(input_file_hits, output_file_hits_1,
output_file_hits_2):
if (not os.path.isfile(output_file_hits_1)
and not os.path.isfile(output_file_hits_2)
) or local_configuration['overwrite_output_files']:
with tb.openFile(input_file_hits, mode="r") as in_hit_file_h5:
hit_table_in = in_hit_file_h5.root.Hits
with tb.openFile(output_file_hits_1,
mode="w") as out_hit_file_1_h5:
with tb.openFile(output_file_hits_2,
mode="w") as out_hit_file_2_h5:
hit_table_out_1 = out_hit_file_1_h5.createTable(
out_hit_file_1_h5.root,
name='Hits',
description=data_struct.HitInfoTable,
title='hit_data',
filters=tb.Filters(complib='blosc',
complevel=5,
fletcher32=False))
hit_table_out_2 = out_hit_file_2_h5.createTable(
out_hit_file_2_h5.root,
name='Hits',
description=data_struct.HitInfoTable,
title='hit_data',
filters=tb.Filters(complib='blosc',
complevel=5,
fletcher32=False))
progress_bar = progressbar.ProgressBar(
widgets=[
'',
progressbar.Percentage(), ' ',
progressbar.Bar(marker='*', left='|', right='|'),
' ',
analysis_utils.ETA()
],
maxval=hit_table_in.shape[0],
term_width=80)
progress_bar.start()
for data, index in analysis_utils.data_aligned_at_events(
hit_table_in, chunk_size=5000000):
hit_table_out_1.append(
data[data['LVL1ID'] %
2 == 1]) # first trigger hits
hit_table_out_2.append(
data[data['LVL1ID'] %
2 == 0]) # second trigger hits
progress_bar.update(index)
progress_bar.finish()
in_hit_file_h5.root.meta_data.copy(
out_hit_file_1_h5.root
) # copy meta_data note to new file
in_hit_file_h5.root.meta_data.copy(
out_hit_file_2_h5.root
) # copy meta_data note to new file
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)