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
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]
n_injections = [int(item[1]) for item in run_config if item[0] == 'n_injections'][0]
hit_data = hit_data[hit_data['data_header'] == 1]
param_range = np.unique(meta_data['scan_param_id'])
self.logger.info('Get the global threshold distributions for all pixels...')
scurve = analysis.scurve_hist(hit_data, param_range)
self.logger.info('Fit the scurves for all pixels...')
thr2D, sig2D, chi2ndf2D = analysis.fit_scurves_multithread(scurve, scan_param_range=range(Vthreshold_start, Vthreshold_stop), n_injections=n_injections, invert_x=True)
h5_file.create_group(h5_file.root, 'interpreted', 'Interpreted Data')
h5_file.create_table(h5_file.root.interpreted, 'hit_data', hit_data, filters=tb.Filters(complib='zlib', complevel=5))
h5_file.create_carray(h5_file.root.interpreted, name='HitDistribution', obj=scurve)
h5_file.create_carray(h5_file.root.interpreted, name='PixelThresholdMap', obj=thr2D.T)
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
hit_data = analysis.interpret_raw_data(raw_data, meta_data)
hit_data = hit_data[hit_data['data_header'] == 1]
param_range = np.unique(meta_data['scan_param_id'])
scurve = analysis.scurve_hist(hit_data, param_range)
n_injections = [int(item[1]) for item in run_config if item[0] == 'n_injections'][0]
VTP_fine_start = [int(item[1]) for item in run_config if item[0] == 'VTP_fine_start'][0]
VTP_fine_stop = [int(item[1]) for item in run_config if item[0] == 'VTP_fine_stop'][0]
param_range = range(VTP_fine_start, VTP_fine_stop)
thr2D, sig2D, chi2ndf2D = analysis.fit_scurves_multithread(scurve, scan_param_range=param_range, n_injections=n_injections)
h5_file.create_group(h5_file.root, 'interpreted', 'Interpreted Data')
h5_file.create_table(h5_file.root.interpreted, 'hit_data', hit_data, filters=tb.Filters(complib='zlib', complevel=5))
h5_file.create_carray(h5_file.root.interpreted, name='HistSCurve', obj=scurve)
h5_file.create_carray(h5_file.root.interpreted, name='Chi2Map', obj=chi2ndf2D.T)
h5_file.create_carray(h5_file.root.interpreted, name='ThresholdMap', obj=thr2D.T)
h5_file.create_carray(h5_file.root.interpreted, name='NoiseMap', obj=sig2D.T)
pix_occ = np.bincount(hit_data['x'] * 256 + hit_data['y'], minlength=256 * 256).astype(np.uint32)
hist_occ = np.reshape(pix_occ, (256, 256)).T
h5_file.create_carray(h5_file.root.interpreted, name='HistOcc', obj=hist_occ)
def analyze(self, progress = None, status = None, **kwargs):
'''
Analyze the data of the scan
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 scan
with tb.open_file(h5_filename, 'r+') as h5_file:
# Read raw data, meta data and configuration parameters
raw_data = h5_file.root.raw_data[:]
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]
# Create group to save all data and histograms to the HDF file
h5_file.create_group(h5_file.root, 'interpreted', 'Interpreted Data')
self.logger.info('Interpret raw data...')
# Interpret the raw data (2x 32 bit to 1x 48 bit)
hit_data = analysis.interpret_raw_data(raw_data, op_mode, vco, meta_data, progress = progress)
raw_data = None
# Select only data which is hit data
hit_data = hit_data[hit_data['data_header'] == 1]
h5_file.create_table(h5_file.root.interpreted, 'hit_data', hit_data, filters=tb.Filters(complib='zlib', complevel=5))
pix_occ = np.bincount(hit_data['x'] * 256 + hit_data['y'], minlength=256 * 256).astype(np.uint32)
hist_occ = np.reshape(pix_occ, (256, 256)).T
h5_file.create_carray(h5_file.root.interpreted, name='HistOcc', obj=hist_occ)
param_range = np.unique(meta_data['scan_param_id'])
meta_data = None
pix_occ = None
hist_occ = None
# Create histograms for number of detected hits for individual thresholds
scurve = analysis.scurve_hist(hit_data, param_range)
hit_data = None
# Read needed configuration parameters
n_injections = [int(item[1]) for item in run_config if item[0] == b'n_injections'][0]
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]
# Fit S-Curves to the histogramms for all pixels
param_range = list(range(Vthreshold_start, Vthreshold_stop))
thr2D, sig2D, chi2ndf2D = analysis.fit_scurves_multithread(scurve, scan_param_range=param_range, n_injections=n_injections, invert_x=True, progress = progress)
h5_file.create_carray(h5_file.root.interpreted, name='HistSCurve', obj=scurve)
h5_file.create_carray(h5_file.root.interpreted, name='Chi2Map', obj=chi2ndf2D.T)
h5_file.create_carray(h5_file.root.interpreted, name='ThresholdMap', obj=thr2D.T)
h5_file.create_carray(h5_file.root.interpreted, name='NoiseMap', obj=sig2D.T)
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
self.logger.info('Interpret raw data...')
hit_data = analysis.interpret_raw_data(raw_data, meta_data)
print(hit_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]
print(hit_data)
param_range = np.unique(meta_data['scan_param_id'])
self.logger.info(
'Get the global threshold distributions for all pixels...')
scurve = analysis.scurve_hist(hit_data, param_range)
self.logger.info(
'Calculate the mean of the global threshold distributions for all pixels...'
)
vths = analysis.vths(scurve, param_range, Vthreshold_start)
h5_file.create_group(h5_file.root, 'interpreted',
'Interpreted Data')
h5_file.create_table(h5_file.root.interpreted,
'hit_data',
hit_data,
filters=tb.Filters(complib='zlib',
complevel=5))
h5_file.create_carray(h5_file.root.interpreted,
name='HitDistribution',
obj=scurve)
h5_file.create_carray(h5_file.root.interpreted,
name='PixelThresholdMap',
obj=vths.T)
def take_data():
self.fifo_readout.reset_rx()
self.fifo_readout.enable_rx(True)
time.sleep(0.2)
mask_step_cmd = []
for i in range(256 / 4):
mask_step_cmd.append(
self.chip.write_pcr(range(4 * i, 4 * i + 4), write=False))
mask_step_cmd.append(self.chip.read_pixel_matrix_datadriven())
self.chip.write(mask_step_cmd)
stop_cmd = []
stop_cmd.append(self.chip.stop_readout(write=False))
#stop_cmd.append(self.chip.set_dac("Vthreshold_coarse", 15, write=False))
stop_cmd.append(self.chip.reset_sequential(write=False))
with self.readout(scan_param_id=1,
fill_buffer=True,
clear_buffer=True):
time.sleep(0.1)
#self.chip.set_dac("Vthreshold_coarse", 6)
time.sleep(0.01)
with self.shutter():
time.sleep(wait_time)
self.chip.write(stop_cmd)
time.sleep(0.1)
dqdata = self.fifo_readout.data
raw_data = np.concatenate([item[0] for item in dqdata])
error = (len(raw_data) % 2 != 0)
hit_data = analysis.interpret_raw_data(raw_data)
error |= (self.chip['RX'].LOST_DATA_COUNTER > 0)
error |= (self.chip['RX'].DECODER_ERROR_COUNTER > 0)
if error:
self.logger.error('DATA ERROR')
hit_data = hit_data[hit_data['data_header'] == 1]
#self.logger.info('raw_data = %d, hit_data = %d' % (len(raw_data), len(hit_data)))
bc = np.bincount(hit_data['x'].astype(np.uint16) * 256 +
hit_data['y'],
minlength=256 * 256)
hist_occ = np.reshape(bc, (256, 256))
return hist_occ
def interpret_data(self, data):
''' Called for every chunk received '''
if isinstance(data[0][1], dict): # Meta data
return self._interpret_meta_data(data)
raw_data = data[0][1]
hit_data = analysis.interpret_raw_data(data[0][1])
hit_data = hit_data[hit_data['data_header'] == 1]
pix_occ = np.bincount(hit_data['x'] * 256 + hit_data['y'], minlength=256*256).astype(np.uint32)
hist_occ = np.reshape(pix_occ, (256, 256))
hit_count = np.count_nonzero(hist_occ.flat)
self.total_hits += len(hit_data)
self.readout += 1
self.total_events = self.readout # ???
if hit_count > 1: #cut noise
self.hist_hit_count[hit_count] += 1
self.hist_occ += hist_occ
#TODO: self.hist_tot ...
interpreted_data = {
#'hits': hit_data,
'occupancy': self.hist_occ,
'tot_hist': self.hist_tot,
'hist_hit_count': self.hist_hit_count,
'hist_event_status': []
}
if self.int_readouts != 0: # = 0 for infinite integration
if self.readout % self.int_readouts == 0:
self.reset_hists()
return [interpreted_data]
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(self, progress=None, status=None, **kwargs):
'''
Analyze the data of the scan
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 calibration
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]
# Create group to save all data and histograms to the HDF file
try:
h5_file.remove_node(h5_file.root.interpreted, recursive=True)
except:
pass
h5_file.create_group(h5_file.root, 'interpreted',
'Interpreted Data')
self.logger.info('Interpret raw data...')
param_range = np.unique(meta_data['scan_param_id'])
# Create arrays for interpreted data for all scan parameter IDs
totcurves_means = np.zeros((256 * 256, len(param_range)),
dtype=np.uint16)
totcurves_hits = np.zeros((256 * 256, len(param_range)),
dtype=np.uint16)
if progress == None:
pbar = tqdm(total=len(param_range))
else:
step_counter = 0
# Interpret data seperately per scan parameter id to save RAM
for param_id in param_range:
start_index = meta_data[meta_data['scan_param_id'] == param_id]
stop_index = meta_data[meta_data['scan_param_id'] == param_id]
# Interpret the raw data (2x 32 bit to 1x 48 bit)
raw_data_tmp = h5_file.root.raw_data[
start_index['index_start'][0]:stop_index['index_stop'][-1]]
hit_data_tmp = analysis.interpret_raw_data(raw_data_tmp,
op_mode,
vco,
progress=progress)
raw_data_tmp = None
# Select only data which is hit data
hit_data_tmp = hit_data_tmp[hit_data_tmp['data_header'] == 1]
# Create histograms for number of detected ToT clock cycles for individual testpulses
full_tmp, count_tmp = analysis.totcurve_hist(hit_data_tmp)
# Put results of current scan parameter ID in overall arrays
totcurves_means[:, param_id] = full_tmp
full_tmp = None
totcurves_hits[:, param_id] = count_tmp
count_tmp = None
hit_data_tmp = None
if progress == None:
pbar.update(1)
else:
step_counter += 1
fraction = step_counter / (len(param_range))
progress.put(fraction)
if progress == None:
pbar.close()
meta_data = None
# Calculate the mean ToT per pixel per pulse
totcurve = np.divide(totcurves_means,
totcurves_hits,
where=totcurves_hits > 0)
totcurve = np.nan_to_num(totcurve)
# Only use pixel which saw exactly all pulses
totcurve[totcurves_hits != 10] = 0
hit_data = None
# Read needed configuration parameters
VTP_fine_start = [
int(item[1]) for item in run_config
if item[0] == b'VTP_fine_start'
][0]
VTP_fine_stop = [
int(item[1]) for item in run_config
if item[0] == b'VTP_fine_stop'
][0]
# Fit ToT-Curves to the histogramms for all pixels
param_range = list(range(VTP_fine_start, VTP_fine_stop))
h5_file.create_carray(h5_file.root.interpreted,
name='HistToTCurve',
obj=totcurve)
h5_file.create_carray(h5_file.root.interpreted,
name='HistToTCurve_Full',
obj=totcurves_means)
h5_file.create_carray(h5_file.root.interpreted,
name='HistToTCurve_Count',
obj=totcurves_hits)
mean, popt, pcov = analysis.fit_totcurves_mean(
totcurve, scan_param_range=param_range, progress=progress)
h5_file.create_table(h5_file.root.interpreted, 'mean_curve', mean)
data_type = {
'names': ['param', 'value', 'stddev'],
'formats': ['S1', 'float32', 'float32']
}
parameter_table = np.recarray(4, dtype=data_type)
parameter_table['param'] = ['a', 'b', 'c', 't']
parameter_table['value'] = [popt[0], popt[1], popt[2], popt[3]]
parameter_table['stddev'] = [
np.sqrt(pcov[0][0]),
np.sqrt(pcov[1][1]),
np.sqrt(pcov[2][2]),
np.sqrt(pcov[3][3])
]
h5_file.create_table(h5_file.root.interpreted, 'fit_params',
parameter_table)
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,
file_name,
args,
cluster_radius=1.1,
cluster_dt=5,
progress=None):
big = args_dict["big"]
new_file = args_dict["new_file"]
self.logger.info('Starting data analysis of ' + str(file_name) +
' ...')
if new_file:
output_filename = self.create_output_file(file_name)
else:
output_filename = file_name
#if file_name != "":
self.h5_filename = file_name
self.h5_filename_out = output_filename
file_extension = file_name.split('/')[-1]
#with tb.open_file(self.h5_filename, 'r+') as h5_file_in:
h5_file_in = tb.open_file(self.h5_filename, 'r+')
meta_data = h5_file_in.root.meta_data[:]
run_config = h5_file_in.root.configuration.run_config[:]
general_config = h5_file_in.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]
vco = False
with tb.open_file(self.h5_filename_out, 'r+') as h5_file:
# create structures to write the hit_data and cluster data in
try:
h5_file.remove_node(h5_file.root.interpreted, recursive=True)
print("Node interpreted allready there")
except:
print("Create node interpreted")
h5_file.create_group(h5_file.root, 'interpreted',
'Interpreted Data')
try:
h5_file.remove_node(h5_file.root.reconstruction,
recursive=True)
print("Node reconstrution allready there")
except:
print("Create node reconstrution")
h5_file.create_group(h5_file.root, 'reconstruction',
'Reconstructed Data')
# for large data_sets we might want to split it into smaller parts to speed up analysis and save RAM
if big == True:
# customize number of meta data chunks to be analyzed at once here
chunk_length = 3000
meta_length = len(meta_data)
# array of indices of the meta_data chunks each package of chunks begins with
iteration_array = range(0, meta_length, chunk_length)
# for smaller data we just analyse everything at once -> only one set chunks, involving all data
else:
iteration_array = [0]
cluster_sum = 0
cluster_sum_g1 = 0
hit_sum = 0
hit_sum_b = 0
hit_index = 0
# iterate over all sets of chunks
for num, i in enumerate(iteration_array):
# Split meta_data
if big == False: # take all data
self.logger.info("Start analysis of part 1/1")
meta_data_tmp = meta_data[:]
elif i < meta_length - chunk_length: # take all data in chunks
self.logger.info(
"Start analysis of part %d/%d" %
(num + 1, math.ceil(meta_length / chunk_length)))
meta_data_tmp = meta_data[i:i + chunk_length]
else: # take all data until the end
self.logger.info(
"Start analysis of part %d/%d" %
(num + 1, math.ceil(meta_length / chunk_length)))
meta_data_tmp = meta_data[i:]
# get raw_data
raw_data_tmp = h5_file_in.root.raw_data[meta_data_tmp[
'index_start'][0]:meta_data_tmp['index_stop'][-1]]
# shift indices in meta_data to start a zero
start = meta_data_tmp['index_start'][0]
meta_data_tmp[
'index_start'] = meta_data_tmp['index_start'] - start
meta_data_tmp[
'index_stop'] = meta_data_tmp['index_stop'] - start
# analyze data
hit_data_tmp = analysis.interpret_raw_data(raw_data_tmp,
op_mode,
vco,
meta_data_tmp,
split_fine=True)
print(hit_data_tmp.shape[0])
if hit_data_tmp.shape[0] != 0:
hit_data_tmp = hit_data_tmp[hit_data_tmp['data_header'] ==
1]
hit_data_tmp['hit_index'] = range(
hit_index, hit_index + hit_data_tmp.shape[0])
hit_index += hit_data_tmp.shape[0]
# cluster data
self.logger.info("Start clustering...")
cluster_data = self.cluster(hit_data_tmp, cluster_radius,
cluster_dt)
self.logger.info("Done with clustering.")
# save hit_data
h5_file.create_table(h5_file.root.interpreted,
'hit_data_' + str(num),
hit_data_tmp,
filters=tb.Filters(complib='zlib',
complevel=5))
# create group for cluster data
group = h5_file.create_group(
h5_file.root.reconstruction, 'run_' + str(num),
'Cluster Data of Chunk ' + str(num))
# write cluster data into h5 file
self.logger.info("Start writing into h5 file...")
vlarray = h5_file.create_vlarray(group,
'x',
tb.Int32Atom(shape=()),
"x-values",
filters=tb.Filters(
complib='zlib',
complevel=5))
for i in range(cluster_data.shape[0]):
vlarray.append(cluster_data['x'][i])
vlarray = h5_file.create_vlarray(group,
'y',
tb.Int32Atom(shape=()),
"y-values",
filters=tb.Filters(
complib='zlib',
complevel=5))
for i in range(cluster_data.shape[0]):
vlarray.append(cluster_data['y'][i])
vlarray = h5_file.create_vlarray(group,
'TOA',
tb.Int64Atom(shape=()),
"TOA-values",
filters=tb.Filters(
complib='zlib',
complevel=5))
for i in range(cluster_data.shape[0]):
vlarray.append(cluster_data['TOA'][i])
vlarray = h5_file.create_vlarray(group,
'TOT',
tb.Int32Atom(shape=()),
"TOT-values",
filters=tb.Filters(
complib='zlib',
complevel=5))
for i in range(cluster_data.shape[0]):
vlarray.append(cluster_data['TOT'][i])
vlarray = h5_file.create_vlarray(group,
'EventCounter',
tb.Int32Atom(shape=()),
"EventCounter-values",
filters=tb.Filters(
complib='zlib',
complevel=5))
for i in range(cluster_data.shape[0]):
vlarray.append(cluster_data['EventCounter'][i])
vlarray = h5_file.create_vlarray(group,
'TOA_Extension',
tb.Int64Atom(shape=()),
"TOA_Extension-values",
filters=tb.Filters(
complib='zlib',
complevel=5))
for i in range(cluster_data.shape[0]):
vlarray.append(cluster_data['TOA_Extension'][i])
vlarray = h5_file.create_vlarray(group,
'hit_index',
tb.Int64Atom(shape=()),
"hit_index-values",
filters=tb.Filters(
complib='zlib',
complevel=5))
for i in range(cluster_data.shape[0]):
vlarray.append(cluster_data['hit_index'][i])
vlarray = h5_file.create_array(group, 'cluster_nr',
cluster_data['cluster_nr'],
"cluster_nr-values")
h5_file.create_array(group, 'chunk_start_time',
cluster_data['chunk_start_time'],
"chunk_start_time-values")
h5_file.create_array(group, 'hits', cluster_data['hits'],
"size of cluster")
h5_file.create_array(group, 'centerX',
cluster_data['centerX'],
"mean of the x values")
h5_file.create_array(group, 'centerY',
cluster_data['centerY'],
"mean of the y values")
h5_file.create_array(group, 'sumTOT',
cluster_data['sumTOT'],
"sum of the ToT in the cluster")
# print out cluster information
print("# cluster in chunk: " +
str(len(cluster_data['hits'])))
if len(cluster_data['hits']) != 0:
print("average size: " +
str(np.mean(cluster_data['hits'])))
print("total hits in chunk: " +
str(np.sum(cluster_data['hits'])))
cluster_sum += len(cluster_data['hits'])
cluster_sum_g1 += len(
cluster_data['hits'][cluster_data['hits'] > 1])
hit_sum += np.sum(cluster_data['hits'])
hit_sum_b += hit_data_tmp.shape[0]
# print out final information on clustering
print("# cluster in total: " + str(cluster_sum))
print("# cluster with more than one hit: " + str(cluster_sum_g1))
print("# hits in total: " + str(hit_sum))
print("# hits in total alternative calc: " + str(hit_sum))
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