def analyze(self):
h5_filename = self.output_filename + '.h5'
self.logger.info('Starting data analysis...')
with tb.open_file(h5_filename, 'r+') as h5_file:
raw_data = h5_file.root.raw_data[:]
meta_data = h5_file.root.meta_data[:]
run_config = h5_file.root.configuration.run_config[:]
# TODO: TMP this should go to analysis function with chunking
#print('haeder1\t header2\t y\t x\t Hits\t Counter')
self.logger.info('Interpret raw data...')
hit_data = analysis.interpret_raw_data(raw_data, meta_data)
Vthreshold_start = [int(item[1]) for item in run_config if item[0] == 'Vthreshold_start'][0]
Vthreshold_stop = [int(item[1]) for item in run_config if item[0] == 'Vthreshold_stop'][0]
hit_data = hit_data[hit_data['data_header'] == 1]
param_range = np.unique(meta_data['scan_param_id'])
hit_data_th0 = hit_data[hit_data['scan_param_id'] < len(param_range) / 2]
param_range_th0 = np.unique(hit_data_th0['scan_param_id'])
hit_data_th15 = hit_data[hit_data['scan_param_id'] >= len(param_range) / 2]
param_range_th15 = np.unique(hit_data_th15['scan_param_id'])
self.logger.info('Get the global threshold distributions for all pixels...')
scurve_th0 = analysis.scurve_hist(hit_data_th0, param_range_th0)
scurve_th15 = analysis.scurve_hist(hit_data_th15, param_range_th15)
self.logger.info('Calculate the mean of the global threshold distributions for all pixels...')
vths_th0 = analysis.vths(scurve_th0, param_range_th0, Vthreshold_start)
vths_th15 = analysis.vths(scurve_th15, param_range_th15, Vthreshold_start)
self.logger.info('Get the cumulated global threshold distributions...')
hist_th0 = analysis.vth_hist(vths_th0, Vthreshold_stop)
hist_th15 = analysis.vth_hist(vths_th15, Vthreshold_stop)
self.logger.info('Calculate the equalisation matrix...')
eq_matrix = analysis.eq_matrix(hist_th0, hist_th15, vths_th0, Vthreshold_start, Vthreshold_stop)
mask_matrix = np.zeros((256, 256), dtype=np.bool)
mask_matrix[:, :] = 0
self.logger.info('Writing mask_matrix to file...')
maskfile = os.path.join(self.working_dir, self.timestamp + '_mask.h5')
with tb.open_file(maskfile, 'a') as out_file:
try:
out_file.remove_node(out_file.root.mask_matrix)
except NoSuchNodeError:
self.logger.debug('Specified maskfile does not include a mask_matrix yet!')
out_file.create_carray(out_file.root,
name='mask_matrix',
title='Matrix mask',
obj=mask_matrix)
self.logger.info('Closing mask file: %s' % (maskfile))
self.logger.info('Writing equalisation matrix to file...')
with tb.open_file(maskfile, 'a') as out_file:
try:
out_file.remove_node(out_file.root.thr_matrix)
except NoSuchNodeError:
self.logger.debug('Specified maskfile does not include a thr_mask yet!')
out_file.create_carray(out_file.root,
name='thr_matrix',
title='Matrix Threshold',
obj=eq_matrix)
self.logger.info('Closing equalisation matrix file: %s' % (maskfile))
def analyze(self, progress=None, status=None, result_path=None, **kwargs):
'''
Analyze the data of the equalisation and calculate the equalisation matrix
If progress is None a tqdm progress bar is used else progress should be a Multiprocess Queue which stores the progress as fraction of 1
If there is a status queue information about the status of the scan are put into it
'''
h5_filename = self.output_filename + '.h5'
self.logger.info('Starting data analysis...')
if status != None:
status.put("Performing data analysis")
# Open the HDF5 which contains all data of the equalisation
with tb.open_file(h5_filename, 'r+') as h5_file:
# Read raw data, meta data and configuration parameters
meta_data = h5_file.root.meta_data[:]
run_config = h5_file.root.configuration.run_config[:]
general_config = h5_file.root.configuration.generalConfig[:]
op_mode = [
row[1] for row in general_config if row[0] == b'Op_mode'
][0]
vco = [
row[1] for row in general_config if row[0] == b'Fast_Io_en'
][0]
self.logger.info('Interpret raw data...')
# THR = 0
param_range, index = np.unique(meta_data['scan_param_id'],
return_index=True)
meta_data_th0 = meta_data[
meta_data['scan_param_id'] < len(param_range) // 2]
param_range_th0 = np.unique(meta_data_th0['scan_param_id'])
# THR = 15
meta_data_th15 = meta_data[
meta_data['scan_param_id'] >= len(param_range) // 2]
param_range_th15 = np.unique(meta_data_th15['scan_param_id'])
# shift indices so that they start with zero
start = meta_data_th15['index_start'][0]
meta_data_th15[
'index_start'] = meta_data_th15['index_start'] - start
meta_data_th15['index_stop'] = meta_data_th15['index_stop'] - start
self.logger.info('THR = 0')
#THR = 0
raw_data_thr0 = h5_file.root.raw_data[:meta_data_th0['index_stop']
[-1]]
hit_data_thr0 = analysis.interpret_raw_data(raw_data_thr0,
op_mode,
vco,
meta_data_th0,
progress=progress)
raw_data_thr0 = None
self.logger.info('THR = 15')
#THR = 15
raw_data_thr15 = h5_file.root.raw_data[
meta_data_th0['index_stop'][-1]:]
hit_data_thr15 = analysis.interpret_raw_data(raw_data_thr15,
op_mode,
vco,
meta_data_th15,
progress=progress)
raw_data_thr15 = None
# Read needed configuration parameters
Vthreshold_start = [
int(item[1]) for item in run_config
if item[0] == b'Vthreshold_start'
][0]
Vthreshold_stop = [
int(item[1]) for item in run_config
if item[0] == b'Vthreshold_stop'
][0]
chip_wafer = [
int(item[1]) for item in run_config if item[0] == b'chip_wafer'
][0]
chip_x = [
item[1].decode() for item in run_config if item[0] == b'chip_x'
][0]
chip_y = [int(item[1]) for item in run_config
if item[0] == b'chip_y'][0]
# Select only data which is hit data
hit_data_thr0 = hit_data_thr0[hit_data_thr0['data_header'] == 1]
hit_data_thr15 = hit_data_thr15[hit_data_thr15['data_header'] == 1]
# Divide the data into two parts - data for pixel threshold 0 and 15
param_range = np.unique(meta_data['scan_param_id'])
meta_data = None
param_range_th0 = np.unique(hit_data_thr0['scan_param_id'])
param_range_th15 = np.unique(hit_data_thr15['scan_param_id'])
# Create histograms for number of detected hits for individual thresholds
self.logger.info(
'Get the global threshold distributions for all pixels...')
scurve_th0 = analysis.scurve_hist(hit_data_thr0, param_range_th0)
hit_data_thr0 = None
scurve_th15 = analysis.scurve_hist(hit_data_thr15, param_range_th15)
hit_data_thr15 = None
# Calculate the mean of the threshold distributions for all pixels
self.logger.info(
'Calculate the mean of the global threshold distributions for all pixels...'
)
vths_th0 = analysis.vths(scurve_th0, param_range_th0, Vthreshold_start)
scurve_th0 = None
vths_th15 = analysis.vths(scurve_th15, param_range_th15,
Vthreshold_start)
scurve_th15 = None
# Get the treshold distributions for both scan
self.logger.info('Get the cumulated global threshold distributions...')
hist_th0 = analysis.vth_hist(vths_th0, Vthreshold_stop)
hist_th15 = analysis.vth_hist(vths_th15, Vthreshold_stop)
vths_th15 = None
# Use the threshold histogramms and one threshold distribution to calculate the equalisation
self.logger.info('Calculate the equalisation matrix...')
eq_matrix = analysis.eq_matrix(hist_th0, hist_th15, vths_th0,
Vthreshold_start, Vthreshold_stop)
# Don't mask any pixels in the mask file
mask_matrix = np.zeros((256, 256), dtype=np.bool)
mask_matrix[:, :] = 0
# Write the equalisation matrix to a new HDF5 file
self.save_thr_mask(eq_matrix, chip_wafer, chip_x, chip_y)
if result_path != None:
result_path.put(self.thrfile)
def analyze_iteration(self, iteration=0, progress=None, status=None):
'''
Analyze the data of the iteration and calculate the new Ibias_PixelDAC value.
In the last iteration the data is also used to calculate an equalisation matrix.
If progress is None a tqdm progress bar is used else progress should be a Multiprocess Queue which stores the progress as fraction of 1
If there is a status queue information about the status of the scan are put into it
'''
h5_filename = self.output_filename + '.h5'
self.logger.info('Starting data analysis...')
if status != None:
status.put("Performing data analysis")
# Open the HDF5 which contains all data of the optimization iteration
with tb.open_file(h5_filename, 'r+') as h5_file:
# Read raw data, meta data and configuration parameters for the current iteration
meta_data_call = ('h5_file.root.' + 'meta_data_' + str(iteration) +
'[:]')
meta_data = eval(meta_data_call)
run_config_call = ('h5_file.root.' + 'configuration.run_config_' +
str(iteration) + '[:]')
run_config = eval(run_config_call)
general_config_call = ('h5_file.root.' +
'configuration.generalConfig_' +
str(iteration) + '[:]')
general_config = eval(general_config_call)
op_mode = [
row[1] for row in general_config if row[0] == b'Op_mode'
][0]
vco = [
row[1] for row in general_config if row[0] == b'Fast_Io_en'
][0]
self.logger.info('Interpret raw data...')
# THR = 0
param_range, index = np.unique(meta_data['scan_param_id'],
return_index=True)
meta_data_th0 = meta_data[
meta_data['scan_param_id'] < len(param_range) // 2]
param_range_th0 = np.unique(meta_data_th0['scan_param_id'])
# THR = 15
meta_data_th15 = meta_data[
meta_data['scan_param_id'] >= len(param_range) // 2]
param_range_th15 = np.unique(meta_data_th15['scan_param_id'])
# shift indices so that they start with zero
start = meta_data_th15['index_start'][0]
meta_data_th15[
'index_start'] = meta_data_th15['index_start'] - start
meta_data_th15['index_stop'] = meta_data_th15['index_stop'] - start
self.logger.info('THR = 0')
#THR = 0
raw_data_call = ('h5_file.root.' + 'raw_data_' + str(iteration) +
'[:' + meta_data_th0['index_stop'][-1] + ']')
raw_data_thr0 = eval(raw_data_call)
hit_data_thr0 = analysis.interpret_raw_data(raw_data_thr0,
op_mode,
vco,
meta_data_th0,
progress=progress)
raw_data_thr0 = None
self.logger.info('THR = 15')
#THR = 15
raw_data_call = ('h5_file.root.' + 'raw_data_' + str(iteration) +
'[' + meta_data_th0['index_stop'][-1] + ':]')
raw_data_thr15 = eval(raw_data_call)
hit_data_thr15 = analysis.interpret_raw_data(raw_data_thr15,
op_mode,
vco,
meta_data_th15,
progress=progress)
raw_data_thr15 = None
# Read needed configuration parameters
Vthreshold_start = [
int(item[1]) for item in run_config
if item[0] == b'Vthreshold_start'
][0]
Vthreshold_stop = [
int(item[1]) for item in run_config
if item[0] == b'Vthreshold_stop'
][0]
n_injections = [
int(item[1]) for item in run_config if item[0] == b'n_injections'
][0]
pixeldac = [
int(item[1]) for item in run_config if item[0] == b'pixeldac'
][0]
last_pixeldac = [
int(item[1]) for item in run_config if item[0] == b'last_pixeldac'
][0]
last_delta = [
float(item[1]) for item in run_config if item[0] == b'last_delta'
][0]
chip_wafer = [
int(item[1]) for item in run_config if item[0] == b'chip_wafer'
][0]
chip_x = [
item[1].decode() for item in run_config if item[0] == b'chip_x'
][0]
chip_y = [int(item[1]) for item in run_config
if item[0] == b'chip_y'][0]
# Select only data which is hit data
hit_data_thr0 = hit_data_thr0[hit_data_thr0['data_header'] == 1]
hit_data_thr15 = hit_data_thr15[hit_data_thr15['data_header'] == 1]
# Divide the data into two parts - data for pixel threshold 0 and 15
param_range = np.unique(meta_data['scan_param_id'])
meta_data = None
param_range_th0 = np.unique(hit_data_thr0['scan_param_id'])
param_range_th15 = np.unique(hit_data_thr15['scan_param_id'])
# Create histograms for number of detected hits for individual thresholds
self.logger.info(
'Get the global threshold distributions for all pixels...')
scurve_th0 = analysis.scurve_hist(hit_data_thr0,
np.arange(len(param_range) // 2))
hit_data_thr0 = None
scurve_th15 = analysis.scurve_hist(
hit_data_thr15, np.arange(len(param_range) // 2, len(param_range)))
hit_data_thr15 = None
# Fit S-Curves to the histogramms for all pixels
self.logger.info('Fit the scurves for all pixels...')
thr2D_th0, sig2D_th0, chi2ndf2D_th0 = analysis.fit_scurves_multithread(
scurve_th0,
scan_param_range=list(range(Vthreshold_start, Vthreshold_stop)),
n_injections=n_injections,
invert_x=True,
progress=progress)
scurve_th0 = None
thr2D_th15, sig2D_th15, chi2ndf2D_th15 = analysis.fit_scurves_multithread(
scurve_th15,
scan_param_range=list(range(Vthreshold_start, Vthreshold_stop)),
n_injections=n_injections,
invert_x=True,
progress=progress)
scurve_th15 = None
# Put the threshold distribution based on the fit results in two histogramms
self.logger.info('Get the cumulated global threshold distributions...')
hist_th0 = analysis.vth_hist(thr2D_th0, Vthreshold_stop)
hist_th15 = analysis.vth_hist(thr2D_th15, Vthreshold_stop)
# Use the threshold histogramms to calculate the new Ibias_PixelDAC setting
self.logger.info('Calculate new pixelDAC value...')
pixeldac_result = analysis.pixeldac_opt(hist_th0, hist_th15, pixeldac,
last_pixeldac, last_delta,
Vthreshold_start,
Vthreshold_stop)
delta = pixeldac_result[1]
rms_delta = pixeldac_result[2]
# In the last iteration calculate also the equalisation matrix
if delta > rms_delta - 2 and delta < rms_delta + 2:
# Use the threshold histogramms and one threshold distribution to calculate the equalisation
self.logger.info('Calculate the equalisation matrix...')
eq_matrix = analysis.eq_matrix(hist_th0, hist_th15, thr2D_th0,
Vthreshold_start, Vthreshold_stop)
# Don't mask any pixels in the mask file
mask_matrix = np.zeros((256, 256), dtype=np.bool)
mask_matrix[:, :] = 0
# Write the equalisation matrix to a new HDF5 file
self.save_thr_mask(eq_matrix, chip_wafer, chip_x, chip_y)
self.logger.info(
'Result of iteration: Scan with pixeldac %i - New pixeldac %i. Delta was %f with optimal delta %f'
% (int(pixeldac), int(
pixeldac_result[0]), pixeldac_result[1], pixeldac_result[2]))
return pixeldac_result