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SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
get_hit_rate_correction
|
def get_hit_rate_correction(gdacs, calibration_gdacs, cluster_size_histogram):
'''Calculates a correction factor for single hit clusters at the given GDACs from the cluster_size_histogram via cubic interpolation.
Parameters
----------
gdacs : array like
The GDAC settings where the threshold should be determined from the calibration
calibration_gdacs : array like
GDAC settings used during the source scan for the cluster size calibration.
cluster_size_histogram : numpy.array, shape=(80,336,# of GDACs during calibration)
The calibration array
Returns
-------
numpy.array, shape=(80,336,# of GDACs during calibration)
The threshold values for each pixel at gdacs.
'''
logging.info('Calculate the correction factor for the single hit cluster rate at %d given GDAC settings', len(gdacs))
if len(calibration_gdacs) != cluster_size_histogram.shape[0]:
raise ValueError('Length of the provided pixel GDACs does not match the dimension of the cluster size array')
hist_sum = np.sum(cluster_size_histogram, axis=1)
hist_rel = cluster_size_histogram / hist_sum[:, np.newaxis].astype('f4') * 100.
maximum_rate = np.amax(hist_rel[:, 1])
correction_factor = maximum_rate / hist_rel[:, 1]
# sort arrays since interpolate does not work otherwise
calibration_gdacs_sorted = np.array(calibration_gdacs)
correction_factor_sorted = correction_factor[np.argsort(calibration_gdacs_sorted)]
calibration_gdacs_sorted = np.sort(calibration_gdacs_sorted)
interpolation = interp1d(calibration_gdacs_sorted.tolist(), correction_factor_sorted.tolist(), kind='cubic', bounds_error=True)
return interpolation(gdacs)
|
python
|
def get_hit_rate_correction(gdacs, calibration_gdacs, cluster_size_histogram):
'''Calculates a correction factor for single hit clusters at the given GDACs from the cluster_size_histogram via cubic interpolation.
Parameters
----------
gdacs : array like
The GDAC settings where the threshold should be determined from the calibration
calibration_gdacs : array like
GDAC settings used during the source scan for the cluster size calibration.
cluster_size_histogram : numpy.array, shape=(80,336,# of GDACs during calibration)
The calibration array
Returns
-------
numpy.array, shape=(80,336,# of GDACs during calibration)
The threshold values for each pixel at gdacs.
'''
logging.info('Calculate the correction factor for the single hit cluster rate at %d given GDAC settings', len(gdacs))
if len(calibration_gdacs) != cluster_size_histogram.shape[0]:
raise ValueError('Length of the provided pixel GDACs does not match the dimension of the cluster size array')
hist_sum = np.sum(cluster_size_histogram, axis=1)
hist_rel = cluster_size_histogram / hist_sum[:, np.newaxis].astype('f4') * 100.
maximum_rate = np.amax(hist_rel[:, 1])
correction_factor = maximum_rate / hist_rel[:, 1]
# sort arrays since interpolate does not work otherwise
calibration_gdacs_sorted = np.array(calibration_gdacs)
correction_factor_sorted = correction_factor[np.argsort(calibration_gdacs_sorted)]
calibration_gdacs_sorted = np.sort(calibration_gdacs_sorted)
interpolation = interp1d(calibration_gdacs_sorted.tolist(), correction_factor_sorted.tolist(), kind='cubic', bounds_error=True)
return interpolation(gdacs)
|
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gdacs : array like
The GDAC settings where the threshold should be determined from the calibration
calibration_gdacs : array like
GDAC settings used during the source scan for the cluster size calibration.
cluster_size_histogram : numpy.array, shape=(80,336,# of GDACs during calibration)
The calibration array
Returns
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numpy.array, shape=(80,336,# of GDACs during calibration)
The threshold values for each pixel at gdacs.
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https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L1356-L1385
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
get_mean_threshold_from_calibration
|
def get_mean_threshold_from_calibration(gdac, mean_threshold_calibration):
'''Calculates the mean threshold from the threshold calibration at the given gdac settings. If the given gdac value was not used during caluibration
the value is determined by interpolation.
Parameters
----------
gdacs : array like
The GDAC settings where the threshold should be determined from the calibration
mean_threshold_calibration : pytable
The table created during the calibration scan.
Returns
-------
numpy.array, shape=(len(gdac), )
The mean threshold values at each value in gdacs.
'''
interpolation = interp1d(mean_threshold_calibration['parameter_value'], mean_threshold_calibration['mean_threshold'], kind='slinear', bounds_error=True)
return interpolation(gdac)
|
python
|
def get_mean_threshold_from_calibration(gdac, mean_threshold_calibration):
'''Calculates the mean threshold from the threshold calibration at the given gdac settings. If the given gdac value was not used during caluibration
the value is determined by interpolation.
Parameters
----------
gdacs : array like
The GDAC settings where the threshold should be determined from the calibration
mean_threshold_calibration : pytable
The table created during the calibration scan.
Returns
-------
numpy.array, shape=(len(gdac), )
The mean threshold values at each value in gdacs.
'''
interpolation = interp1d(mean_threshold_calibration['parameter_value'], mean_threshold_calibration['mean_threshold'], kind='slinear', bounds_error=True)
return interpolation(gdac)
|
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gdacs : array like
The GDAC settings where the threshold should be determined from the calibration
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https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L1388-L1405
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
get_pixel_thresholds_from_calibration_array
|
def get_pixel_thresholds_from_calibration_array(gdacs, calibration_gdacs, threshold_calibration_array, bounds_error=True):
'''Calculates the threshold for all pixels in threshold_calibration_array at the given GDAC settings via linear interpolation. The GDAC settings used during calibration have to be given.
Parameters
----------
gdacs : array like
The GDAC settings where the threshold should be determined from the calibration
calibration_gdacs : array like
GDAC settings used during calibration, needed to translate the index of the calibration array to a value.
threshold_calibration_array : numpy.array, shape=(80,336,# of GDACs during calibration)
The calibration array
Returns
-------
numpy.array, shape=(80,336,# gdacs given)
The threshold values for each pixel at gdacs.
'''
if len(calibration_gdacs) != threshold_calibration_array.shape[2]:
raise ValueError('Length of the provided pixel GDACs does not match the third dimension of the calibration array')
interpolation = interp1d(x=calibration_gdacs, y=threshold_calibration_array, kind='slinear', bounds_error=bounds_error)
return interpolation(gdacs)
|
python
|
def get_pixel_thresholds_from_calibration_array(gdacs, calibration_gdacs, threshold_calibration_array, bounds_error=True):
'''Calculates the threshold for all pixels in threshold_calibration_array at the given GDAC settings via linear interpolation. The GDAC settings used during calibration have to be given.
Parameters
----------
gdacs : array like
The GDAC settings where the threshold should be determined from the calibration
calibration_gdacs : array like
GDAC settings used during calibration, needed to translate the index of the calibration array to a value.
threshold_calibration_array : numpy.array, shape=(80,336,# of GDACs during calibration)
The calibration array
Returns
-------
numpy.array, shape=(80,336,# gdacs given)
The threshold values for each pixel at gdacs.
'''
if len(calibration_gdacs) != threshold_calibration_array.shape[2]:
raise ValueError('Length of the provided pixel GDACs does not match the third dimension of the calibration array')
interpolation = interp1d(x=calibration_gdacs, y=threshold_calibration_array, kind='slinear', bounds_error=bounds_error)
return interpolation(gdacs)
|
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The GDAC settings where the threshold should be determined from the calibration
calibration_gdacs : array like
GDAC settings used during calibration, needed to translate the index of the calibration array to a value.
threshold_calibration_array : numpy.array, shape=(80,336,# of GDACs during calibration)
The calibration array
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numpy.array, shape=(80,336,# gdacs given)
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https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L1408-L1428
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SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
get_n_cluster_per_event_hist
|
def get_n_cluster_per_event_hist(cluster_table):
'''Calculates the number of cluster in every event.
Parameters
----------
cluster_table : pytables.table
Returns
-------
numpy.Histogram
'''
logging.info("Histogram number of cluster per event")
cluster_in_events = analysis_utils.get_n_cluster_in_events(cluster_table)[:, 1] # get the number of cluster for every event
return np.histogram(cluster_in_events, bins=range(0, np.max(cluster_in_events) + 2))
|
python
|
def get_n_cluster_per_event_hist(cluster_table):
'''Calculates the number of cluster in every event.
Parameters
----------
cluster_table : pytables.table
Returns
-------
numpy.Histogram
'''
logging.info("Histogram number of cluster per event")
cluster_in_events = analysis_utils.get_n_cluster_in_events(cluster_table)[:, 1] # get the number of cluster for every event
return np.histogram(cluster_in_events, bins=range(0, np.max(cluster_in_events) + 2))
|
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https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L1470-L1483
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SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
get_data_statistics
|
def get_data_statistics(interpreted_files):
'''Quick and dirty function to give as redmine compatible iverview table
'''
print '| *File Name* | *File Size* | *Times Stamp* | *Events* | *Bad Events* | *Measurement time* | *# SR* | *Hits* |' # Mean Tot | Mean rel. BCID'
for interpreted_file in interpreted_files:
with tb.open_file(interpreted_file, mode="r") as in_file_h5: # open the actual hit file
n_hits = np.sum(in_file_h5.root.HistOcc[:])
measurement_time = int(in_file_h5.root.meta_data[-1]['timestamp_stop'] - in_file_h5.root.meta_data[0]['timestamp_start'])
# mean_tot = np.average(in_file_h5.root.HistTot[:], weights=range(0,16) * np.sum(range(0,16)))# / in_file_h5.root.HistTot[:].shape[0]
# mean_bcid = np.average(in_file_h5.root.HistRelBcid[:], weights=range(0,16))
n_sr = np.sum(in_file_h5.root.HistServiceRecord[:])
n_bad_events = int(np.sum(in_file_h5.root.HistErrorCounter[2:]))
try:
n_events = str(in_file_h5.root.Hits[-1]['event_number'] + 1)
except tb.NoSuchNodeError:
n_events = '~' + str(in_file_h5.root.meta_data[-1]['event_number'] + (in_file_h5.root.meta_data[-1]['event_number'] - in_file_h5.root.meta_data[-2]['event_number']))
else:
print '|', os.path.basename(interpreted_file), '|', int(os.path.getsize(interpreted_file) / (1024.0 * 1024.0)), 'Mb |', time.ctime(os.path.getctime(interpreted_file)), '|', n_events, '|', n_bad_events, '|', measurement_time, 's |', n_sr, '|', n_hits, '|'
|
python
|
def get_data_statistics(interpreted_files):
'''Quick and dirty function to give as redmine compatible iverview table
'''
print '| *File Name* | *File Size* | *Times Stamp* | *Events* | *Bad Events* | *Measurement time* | *# SR* | *Hits* |' # Mean Tot | Mean rel. BCID'
for interpreted_file in interpreted_files:
with tb.open_file(interpreted_file, mode="r") as in_file_h5: # open the actual hit file
n_hits = np.sum(in_file_h5.root.HistOcc[:])
measurement_time = int(in_file_h5.root.meta_data[-1]['timestamp_stop'] - in_file_h5.root.meta_data[0]['timestamp_start'])
# mean_tot = np.average(in_file_h5.root.HistTot[:], weights=range(0,16) * np.sum(range(0,16)))# / in_file_h5.root.HistTot[:].shape[0]
# mean_bcid = np.average(in_file_h5.root.HistRelBcid[:], weights=range(0,16))
n_sr = np.sum(in_file_h5.root.HistServiceRecord[:])
n_bad_events = int(np.sum(in_file_h5.root.HistErrorCounter[2:]))
try:
n_events = str(in_file_h5.root.Hits[-1]['event_number'] + 1)
except tb.NoSuchNodeError:
n_events = '~' + str(in_file_h5.root.meta_data[-1]['event_number'] + (in_file_h5.root.meta_data[-1]['event_number'] - in_file_h5.root.meta_data[-2]['event_number']))
else:
print '|', os.path.basename(interpreted_file), '|', int(os.path.getsize(interpreted_file) / (1024.0 * 1024.0)), 'Mb |', time.ctime(os.path.getctime(interpreted_file)), '|', n_events, '|', n_bad_events, '|', measurement_time, 's |', n_sr, '|', n_hits, '|'
|
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Quick and dirty function to give as redmine compatible iverview table
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[
"Quick",
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"function",
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"give",
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"iverview",
"table"
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L1486-L1503
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
contiguous_regions
|
def contiguous_regions(condition):
"""Finds contiguous True regions of the boolean array "condition". Returns
a 2D array where the first column is the start index of the region and the
second column is the end index.
http://stackoverflow.com/questions/4494404/find-large-number-of-consecutive-values-fulfilling-condition-in-a-numpy-array
"""
# Find the indicies of changes in "condition"
d = np.diff(condition, n=1)
idx, = d.nonzero()
# We need to start things after the change in "condition". Therefore,
# we'll shift the index by 1 to the right.
idx += 1
if condition[0]:
# If the start of condition is True prepend a 0
idx = np.r_[0, idx]
if condition[-1]:
# If the end of condition is True, append the length of the array
idx = np.r_[idx, condition.size]
# Reshape the result into two columns
idx.shape = (-1, 2)
return idx
|
python
|
def contiguous_regions(condition):
"""Finds contiguous True regions of the boolean array "condition". Returns
a 2D array where the first column is the start index of the region and the
second column is the end index.
http://stackoverflow.com/questions/4494404/find-large-number-of-consecutive-values-fulfilling-condition-in-a-numpy-array
"""
# Find the indicies of changes in "condition"
d = np.diff(condition, n=1)
idx, = d.nonzero()
# We need to start things after the change in "condition". Therefore,
# we'll shift the index by 1 to the right.
idx += 1
if condition[0]:
# If the start of condition is True prepend a 0
idx = np.r_[0, idx]
if condition[-1]:
# If the end of condition is True, append the length of the array
idx = np.r_[idx, condition.size]
# Reshape the result into two columns
idx.shape = (-1, 2)
return idx
|
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Finds contiguous True regions of the boolean array "condition". Returns
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http://stackoverflow.com/questions/4494404/find-large-number-of-consecutive-values-fulfilling-condition-in-a-numpy-array
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L1519-L1543
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
check_bad_data
|
def check_bad_data(raw_data, prepend_data_headers=None, trig_count=None):
"""Checking FEI4 raw data array for corrupted data.
"""
consecutive_triggers = 16 if trig_count == 0 else trig_count
is_fe_data_header = logical_and(is_fe_word, is_data_header)
trigger_idx = np.where(is_trigger_word(raw_data) >= 1)[0]
fe_dh_idx = np.where(is_fe_data_header(raw_data) >= 1)[0]
n_triggers = trigger_idx.shape[0]
n_dh = fe_dh_idx.shape[0]
# get index of the last trigger
if n_triggers:
last_event_data_headers_cnt = np.where(fe_dh_idx > trigger_idx[-1])[0].shape[0]
if consecutive_triggers and last_event_data_headers_cnt == consecutive_triggers:
if not np.all(trigger_idx[-1] > fe_dh_idx):
trigger_idx = np.r_[trigger_idx, raw_data.shape]
last_event_data_headers_cnt = None
elif last_event_data_headers_cnt != 0:
fe_dh_idx = fe_dh_idx[:-last_event_data_headers_cnt]
elif not np.all(trigger_idx[-1] > fe_dh_idx):
trigger_idx = np.r_[trigger_idx, raw_data.shape]
# if any data header, add trigger for histogramming, next readout has to have trigger word
elif n_dh:
trigger_idx = np.r_[trigger_idx, raw_data.shape]
last_event_data_headers_cnt = None
# no trigger, no data header
# assuming correct data, return input values
else:
return False, prepend_data_headers, n_triggers, n_dh
# # no triggers, check for the right amount of data headers
# if consecutive_triggers and prepend_data_headers and prepend_data_headers + n_dh != consecutive_triggers:
# return True, n_dh, n_triggers, n_dh
n_triggers_cleaned = trigger_idx.shape[0]
n_dh_cleaned = fe_dh_idx.shape[0]
# check that trigger comes before data header
if prepend_data_headers is None and n_triggers_cleaned and n_dh_cleaned and not trigger_idx[0] < fe_dh_idx[0]:
return True, last_event_data_headers_cnt, n_triggers, n_dh # FIXME: 0?
# check that no trigger comes before the first data header
elif consecutive_triggers and prepend_data_headers is not None and n_triggers_cleaned and n_dh_cleaned and trigger_idx[0] < fe_dh_idx[0]:
return True, last_event_data_headers_cnt, n_triggers, n_dh # FIXME: 0?
# check for two consecutive triggers
elif consecutive_triggers is None and prepend_data_headers == 0 and n_triggers_cleaned and n_dh_cleaned and trigger_idx[0] < fe_dh_idx[0]:
return True, last_event_data_headers_cnt, n_triggers, n_dh # FIXME: 0?
elif prepend_data_headers is not None:
trigger_idx += (prepend_data_headers + 1)
fe_dh_idx += (prepend_data_headers + 1)
# for histogramming add trigger at index 0
trigger_idx = np.r_[0, trigger_idx]
fe_dh_idx = np.r_[range(1, prepend_data_headers + 1), fe_dh_idx]
event_hist, bins = np.histogram(fe_dh_idx, trigger_idx)
if consecutive_triggers is None and np.any(event_hist == 0):
return True, last_event_data_headers_cnt, n_triggers, n_dh
elif consecutive_triggers and np.any(event_hist != consecutive_triggers):
return True, last_event_data_headers_cnt, n_triggers, n_dh
return False, last_event_data_headers_cnt, n_triggers, n_dh
|
python
|
def check_bad_data(raw_data, prepend_data_headers=None, trig_count=None):
"""Checking FEI4 raw data array for corrupted data.
"""
consecutive_triggers = 16 if trig_count == 0 else trig_count
is_fe_data_header = logical_and(is_fe_word, is_data_header)
trigger_idx = np.where(is_trigger_word(raw_data) >= 1)[0]
fe_dh_idx = np.where(is_fe_data_header(raw_data) >= 1)[0]
n_triggers = trigger_idx.shape[0]
n_dh = fe_dh_idx.shape[0]
# get index of the last trigger
if n_triggers:
last_event_data_headers_cnt = np.where(fe_dh_idx > trigger_idx[-1])[0].shape[0]
if consecutive_triggers and last_event_data_headers_cnt == consecutive_triggers:
if not np.all(trigger_idx[-1] > fe_dh_idx):
trigger_idx = np.r_[trigger_idx, raw_data.shape]
last_event_data_headers_cnt = None
elif last_event_data_headers_cnt != 0:
fe_dh_idx = fe_dh_idx[:-last_event_data_headers_cnt]
elif not np.all(trigger_idx[-1] > fe_dh_idx):
trigger_idx = np.r_[trigger_idx, raw_data.shape]
# if any data header, add trigger for histogramming, next readout has to have trigger word
elif n_dh:
trigger_idx = np.r_[trigger_idx, raw_data.shape]
last_event_data_headers_cnt = None
# no trigger, no data header
# assuming correct data, return input values
else:
return False, prepend_data_headers, n_triggers, n_dh
# # no triggers, check for the right amount of data headers
# if consecutive_triggers and prepend_data_headers and prepend_data_headers + n_dh != consecutive_triggers:
# return True, n_dh, n_triggers, n_dh
n_triggers_cleaned = trigger_idx.shape[0]
n_dh_cleaned = fe_dh_idx.shape[0]
# check that trigger comes before data header
if prepend_data_headers is None and n_triggers_cleaned and n_dh_cleaned and not trigger_idx[0] < fe_dh_idx[0]:
return True, last_event_data_headers_cnt, n_triggers, n_dh # FIXME: 0?
# check that no trigger comes before the first data header
elif consecutive_triggers and prepend_data_headers is not None and n_triggers_cleaned and n_dh_cleaned and trigger_idx[0] < fe_dh_idx[0]:
return True, last_event_data_headers_cnt, n_triggers, n_dh # FIXME: 0?
# check for two consecutive triggers
elif consecutive_triggers is None and prepend_data_headers == 0 and n_triggers_cleaned and n_dh_cleaned and trigger_idx[0] < fe_dh_idx[0]:
return True, last_event_data_headers_cnt, n_triggers, n_dh # FIXME: 0?
elif prepend_data_headers is not None:
trigger_idx += (prepend_data_headers + 1)
fe_dh_idx += (prepend_data_headers + 1)
# for histogramming add trigger at index 0
trigger_idx = np.r_[0, trigger_idx]
fe_dh_idx = np.r_[range(1, prepend_data_headers + 1), fe_dh_idx]
event_hist, bins = np.histogram(fe_dh_idx, trigger_idx)
if consecutive_triggers is None and np.any(event_hist == 0):
return True, last_event_data_headers_cnt, n_triggers, n_dh
elif consecutive_triggers and np.any(event_hist != consecutive_triggers):
return True, last_event_data_headers_cnt, n_triggers, n_dh
return False, last_event_data_headers_cnt, n_triggers, n_dh
|
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Checking FEI4 raw data array for corrupted data.
|
[
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L1546-L1605
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
consecutive
|
def consecutive(data, stepsize=1):
"""Converts array into chunks with consecutive elements of given step size.
http://stackoverflow.com/questions/7352684/how-to-find-the-groups-of-consecutive-elements-from-an-array-in-numpy
"""
return np.split(data, np.where(np.diff(data) != stepsize)[0] + 1)
|
python
|
def consecutive(data, stepsize=1):
"""Converts array into chunks with consecutive elements of given step size.
http://stackoverflow.com/questions/7352684/how-to-find-the-groups-of-consecutive-elements-from-an-array-in-numpy
"""
return np.split(data, np.where(np.diff(data) != stepsize)[0] + 1)
|
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Converts array into chunks with consecutive elements of given step size.
http://stackoverflow.com/questions/7352684/how-to-find-the-groups-of-consecutive-elements-from-an-array-in-numpy
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L1608-L1612
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
print_raw_data_file
|
def print_raw_data_file(input_file, start_index=0, limit=200, flavor='fei4b', select=None, tdc_trig_dist=False, trigger_data_mode=0, meta_data_v2=True):
"""Printing FEI4 data from raw data file for debugging.
"""
with tb.open_file(input_file + '.h5', mode="r") as file_h5:
if meta_data_v2:
index_start = file_h5.root.meta_data.read(field='index_start')
index_stop = file_h5.root.meta_data.read(field='index_stop')
else:
index_start = file_h5.root.meta_data.read(field='start_index')
index_stop = file_h5.root.meta_data.read(field='stop_index')
total_words = 0
for read_out_index, (index_start, index_stop) in enumerate(np.column_stack((index_start, index_stop))):
if start_index < index_stop:
print "\nchunk %d with length %d (from index %d to %d)\n" % (read_out_index, (index_stop - index_start), index_start, index_stop)
raw_data = file_h5.root.raw_data.read(index_start, index_stop)
total_words += print_raw_data(raw_data=raw_data, start_index=max(start_index - index_start, 0), limit=limit - total_words, flavor=flavor, index_offset=index_start, select=select, tdc_trig_dist=tdc_trig_dist, trigger_data_mode=trigger_data_mode)
if limit and total_words >= limit:
break
|
python
|
def print_raw_data_file(input_file, start_index=0, limit=200, flavor='fei4b', select=None, tdc_trig_dist=False, trigger_data_mode=0, meta_data_v2=True):
"""Printing FEI4 data from raw data file for debugging.
"""
with tb.open_file(input_file + '.h5', mode="r") as file_h5:
if meta_data_v2:
index_start = file_h5.root.meta_data.read(field='index_start')
index_stop = file_h5.root.meta_data.read(field='index_stop')
else:
index_start = file_h5.root.meta_data.read(field='start_index')
index_stop = file_h5.root.meta_data.read(field='stop_index')
total_words = 0
for read_out_index, (index_start, index_stop) in enumerate(np.column_stack((index_start, index_stop))):
if start_index < index_stop:
print "\nchunk %d with length %d (from index %d to %d)\n" % (read_out_index, (index_stop - index_start), index_start, index_stop)
raw_data = file_h5.root.raw_data.read(index_start, index_stop)
total_words += print_raw_data(raw_data=raw_data, start_index=max(start_index - index_start, 0), limit=limit - total_words, flavor=flavor, index_offset=index_start, select=select, tdc_trig_dist=tdc_trig_dist, trigger_data_mode=trigger_data_mode)
if limit and total_words >= limit:
break
|
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Printing FEI4 data from raw data file for debugging.
|
[
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"file",
"for",
"debugging",
"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L1615-L1632
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
print_raw_data
|
def print_raw_data(raw_data, start_index=0, limit=200, flavor='fei4b', index_offset=0, select=None, tdc_trig_dist=False, trigger_data_mode=0):
"""Printing FEI4 raw data array for debugging.
"""
if not select:
select = ['DH', 'TW', "AR", "VR", "SR", "DR", 'TDC', 'UNKNOWN FE WORD', 'UNKNOWN WORD']
total_words = 0
for index in range(start_index, raw_data.shape[0]):
dw = FEI4Record(raw_data[index], chip_flavor=flavor, tdc_trig_dist=tdc_trig_dist, trigger_data_mode=trigger_data_mode)
if dw in select:
print index + index_offset, '{0:12d} {1:08b} {2:08b} {3:08b} {4:08b}'.format(raw_data[index], (raw_data[index] & 0xFF000000) >> 24, (raw_data[index] & 0x00FF0000) >> 16, (raw_data[index] & 0x0000FF00) >> 8, (raw_data[index] & 0x000000FF) >> 0), dw
total_words += 1
if limit and total_words >= limit:
break
return total_words
|
python
|
def print_raw_data(raw_data, start_index=0, limit=200, flavor='fei4b', index_offset=0, select=None, tdc_trig_dist=False, trigger_data_mode=0):
"""Printing FEI4 raw data array for debugging.
"""
if not select:
select = ['DH', 'TW', "AR", "VR", "SR", "DR", 'TDC', 'UNKNOWN FE WORD', 'UNKNOWN WORD']
total_words = 0
for index in range(start_index, raw_data.shape[0]):
dw = FEI4Record(raw_data[index], chip_flavor=flavor, tdc_trig_dist=tdc_trig_dist, trigger_data_mode=trigger_data_mode)
if dw in select:
print index + index_offset, '{0:12d} {1:08b} {2:08b} {3:08b} {4:08b}'.format(raw_data[index], (raw_data[index] & 0xFF000000) >> 24, (raw_data[index] & 0x00FF0000) >> 16, (raw_data[index] & 0x0000FF00) >> 8, (raw_data[index] & 0x000000FF) >> 0), dw
total_words += 1
if limit and total_words >= limit:
break
return total_words
|
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Printing FEI4 raw data array for debugging.
|
[
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"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L1635-L1648
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
ETA.update
|
def update(self, pbar):
'Updates the widget to show the ETA or total time when finished.'
self.n_refresh += 1
if pbar.currval == 0:
return 'ETA: --:--:--'
elif pbar.finished:
return 'Time: %s' % self.format_time(pbar.seconds_elapsed)
else:
elapsed = pbar.seconds_elapsed
try:
speed = pbar.currval / elapsed
if self.speed_smooth is not None:
self.speed_smooth = (self.speed_smooth * (1 - self.SMOOTHING)) + (speed * self.SMOOTHING)
else:
self.speed_smooth = speed
eta = float(pbar.maxval) / self.speed_smooth - elapsed + 1 if float(pbar.maxval) / self.speed_smooth - elapsed + 1 > 0 else 0
if float(pbar.currval) / pbar.maxval > 0.30 or self.n_refresh > 10: # ETA only rather precise if > 30% is already finished or more than 10 times updated
return 'ETA: %s' % self.format_time(eta)
if self.old_eta is not None and self.old_eta < eta: # do not show jumping ETA if non precise mode is active
return 'ETA: ~%s' % self.format_time(self.old_eta)
else:
self.old_eta = eta
return 'ETA: ~%s' % self.format_time(eta)
except ZeroDivisionError:
speed = 0
|
python
|
def update(self, pbar):
'Updates the widget to show the ETA or total time when finished.'
self.n_refresh += 1
if pbar.currval == 0:
return 'ETA: --:--:--'
elif pbar.finished:
return 'Time: %s' % self.format_time(pbar.seconds_elapsed)
else:
elapsed = pbar.seconds_elapsed
try:
speed = pbar.currval / elapsed
if self.speed_smooth is not None:
self.speed_smooth = (self.speed_smooth * (1 - self.SMOOTHING)) + (speed * self.SMOOTHING)
else:
self.speed_smooth = speed
eta = float(pbar.maxval) / self.speed_smooth - elapsed + 1 if float(pbar.maxval) / self.speed_smooth - elapsed + 1 > 0 else 0
if float(pbar.currval) / pbar.maxval > 0.30 or self.n_refresh > 10: # ETA only rather precise if > 30% is already finished or more than 10 times updated
return 'ETA: %s' % self.format_time(eta)
if self.old_eta is not None and self.old_eta < eta: # do not show jumping ETA if non precise mode is active
return 'ETA: ~%s' % self.format_time(self.old_eta)
else:
self.old_eta = eta
return 'ETA: ~%s' % self.format_time(eta)
except ZeroDivisionError:
speed = 0
|
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|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L1442-L1467
|
SiLab-Bonn/pyBAR
|
pybar/scans/analyze_source_scan_gdac_data.py
|
plot_result
|
def plot_result(x_p, y_p, y_p_e, smoothed_data, smoothed_data_diff, filename=None):
''' Fit spline to the profile histogramed data, differentiate, determine MPV and plot.
Parameters
----------
x_p, y_p : array like
data points (x,y)
y_p_e : array like
error bars in y
'''
logging.info('Plot results')
plt.close()
p1 = plt.errorbar(x_p * analysis_configuration['vcal_calibration'], y_p, yerr=y_p_e, fmt='o') # plot data with error bars
p2, = plt.plot(x_p * analysis_configuration['vcal_calibration'], smoothed_data, '-r') # plot smoothed data
factor = np.amax(y_p) / np.amin(smoothed_data_diff) * 1.1
p3, = plt.plot(x_p * analysis_configuration['vcal_calibration'], factor * smoothed_data_diff, '-', lw=2) # plot differentiated data
mpv_index = np.argmax(-analysis_utils.smooth_differentiation(x_p, y_p, weigths=1 / y_p_e, order=3, smoothness=analysis_configuration['smoothness'], derivation=1))
p4, = plt.plot([x_p[mpv_index] * analysis_configuration['vcal_calibration'], x_p[mpv_index] * analysis_configuration['vcal_calibration']], [0, factor * smoothed_data_diff[mpv_index]], 'k-', lw=2)
text = 'MPV ' + str(int(x_p[mpv_index] * analysis_configuration['vcal_calibration'])) + ' e'
plt.text(1.01 * x_p[mpv_index] * analysis_configuration['vcal_calibration'], -10. * smoothed_data_diff[mpv_index], text, ha='left')
plt.legend([p1, p2, p3, p4], ['data', 'smoothed spline', 'spline differentiation', text], prop={'size': 12}, loc=0)
plt.title('\'Single hit cluster\'-occupancy for different pixel thresholds')
plt.xlabel('Pixel threshold [e]')
plt.ylabel('Single hit cluster occupancy [a.u.]')
plt.ylim(0, np.amax(y_p) * 1.15)
if filename is None:
plt.show()
else:
filename.savefig(plt.gcf())
return smoothed_data_diff
|
python
|
def plot_result(x_p, y_p, y_p_e, smoothed_data, smoothed_data_diff, filename=None):
''' Fit spline to the profile histogramed data, differentiate, determine MPV and plot.
Parameters
----------
x_p, y_p : array like
data points (x,y)
y_p_e : array like
error bars in y
'''
logging.info('Plot results')
plt.close()
p1 = plt.errorbar(x_p * analysis_configuration['vcal_calibration'], y_p, yerr=y_p_e, fmt='o') # plot data with error bars
p2, = plt.plot(x_p * analysis_configuration['vcal_calibration'], smoothed_data, '-r') # plot smoothed data
factor = np.amax(y_p) / np.amin(smoothed_data_diff) * 1.1
p3, = plt.plot(x_p * analysis_configuration['vcal_calibration'], factor * smoothed_data_diff, '-', lw=2) # plot differentiated data
mpv_index = np.argmax(-analysis_utils.smooth_differentiation(x_p, y_p, weigths=1 / y_p_e, order=3, smoothness=analysis_configuration['smoothness'], derivation=1))
p4, = plt.plot([x_p[mpv_index] * analysis_configuration['vcal_calibration'], x_p[mpv_index] * analysis_configuration['vcal_calibration']], [0, factor * smoothed_data_diff[mpv_index]], 'k-', lw=2)
text = 'MPV ' + str(int(x_p[mpv_index] * analysis_configuration['vcal_calibration'])) + ' e'
plt.text(1.01 * x_p[mpv_index] * analysis_configuration['vcal_calibration'], -10. * smoothed_data_diff[mpv_index], text, ha='left')
plt.legend([p1, p2, p3, p4], ['data', 'smoothed spline', 'spline differentiation', text], prop={'size': 12}, loc=0)
plt.title('\'Single hit cluster\'-occupancy for different pixel thresholds')
plt.xlabel('Pixel threshold [e]')
plt.ylabel('Single hit cluster occupancy [a.u.]')
plt.ylim(0, np.amax(y_p) * 1.15)
if filename is None:
plt.show()
else:
filename.savefig(plt.gcf())
return smoothed_data_diff
|
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Fit spline to the profile histogramed data, differentiate, determine MPV and plot.
Parameters
----------
x_p, y_p : array like
data points (x,y)
y_p_e : array like
error bars in y
|
[
"Fit",
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/scans/analyze_source_scan_gdac_data.py#L77-L106
|
SiLab-Bonn/pyBAR
|
pybar/scans/calibrate_hit_or.py
|
create_hitor_calibration
|
def create_hitor_calibration(output_filename, plot_pixel_calibrations=False):
'''Generating HitOr calibration file (_calibration.h5) from raw data file and plotting of calibration data.
Parameters
----------
output_filename : string
Input raw data file name.
plot_pixel_calibrations : bool, iterable
If True, genearating additional pixel calibration plots. If list of column and row tuples (from 1 to 80 / 336), print selected pixels.
Returns
-------
nothing
'''
logging.info('Analyze HitOR calibration data and plot results of %s', output_filename)
with AnalyzeRawData(raw_data_file=output_filename, create_pdf=True) as analyze_raw_data: # Interpret the raw data file
analyze_raw_data.create_occupancy_hist = False # too many scan parameters to do in ram histogramming
analyze_raw_data.create_hit_table = True
analyze_raw_data.create_tdc_hist = True
analyze_raw_data.align_at_tdc = True # align events at TDC words, first word of event has to be a tdc word
analyze_raw_data.interpret_word_table()
analyze_raw_data.interpreter.print_summary()
analyze_raw_data.plot_histograms()
n_injections = analyze_raw_data.n_injections # use later
meta_data = analyze_raw_data.out_file_h5.root.meta_data[:]
scan_parameters_dict = get_scan_parameter(meta_data)
inner_loop_parameter_values = scan_parameters_dict[next(reversed(scan_parameters_dict))] # inner loop parameter name is unknown
scan_parameter_names = scan_parameters_dict.keys()
# col_row_combinations = get_unique_scan_parameter_combinations(analyze_raw_data.out_file_h5.root.meta_data[:], scan_parameters=('column', 'row'), scan_parameter_columns_only=True)
meta_data_table_at_scan_parameter = get_unique_scan_parameter_combinations(meta_data, scan_parameters=scan_parameter_names)
scan_parameter_values = get_scan_parameters_table_from_meta_data(meta_data_table_at_scan_parameter, scan_parameter_names)
event_number_ranges = get_ranges_from_array(meta_data_table_at_scan_parameter['event_number'])
event_ranges_per_parameter = np.column_stack((scan_parameter_values, event_number_ranges))
if analyze_raw_data.out_file_h5.root.Hits.nrows == 0:
raise AnalysisError("Found no hits.")
hits = analyze_raw_data.out_file_h5.root.Hits[:]
event_numbers = hits['event_number'].copy() # create contigous array, otherwise np.searchsorted too slow, http://stackoverflow.com/questions/15139299/performance-of-numpy-searchsorted-is-poor-on-structured-arrays
output_filename = os.path.splitext(output_filename)[0]
with tb.open_file(output_filename + "_calibration.h5", mode="w") as calibration_data_file:
logging.info('Create calibration')
calibration_data = np.full(shape=(80, 336, len(inner_loop_parameter_values), 4), fill_value=np.nan, dtype='f4') # result of the calibration is a histogram with col_index, row_index, plsrDAC value, mean discrete tot, rms discrete tot, mean tot from TDC, rms tot from TDC
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.AdaptiveETA()], maxval=len(event_ranges_per_parameter), term_width=80)
progress_bar.start()
for index, (actual_scan_parameter_values, event_start, event_stop) in enumerate(event_ranges_per_parameter):
if event_stop is None: # happens for the last chunk
event_stop = hits[-1]['event_number'] + 1
array_index = np.searchsorted(event_numbers, np.array([event_start, event_stop]))
actual_hits = hits[array_index[0]:array_index[1]]
for item_index, item in enumerate(scan_parameter_names):
if item == "column":
actual_col = actual_scan_parameter_values[item_index]
elif item == "row":
actual_row = actual_scan_parameter_values[item_index]
elif item == "PlsrDAC":
plser_dac = actual_scan_parameter_values[item_index]
else:
raise ValueError("Unknown scan parameter %s" % item)
# Only pixel of actual column/row should be in the actual data chunk but since FIFO is not cleared for each scan step due to speed reasons and there might be noisy pixels this is not always the case
n_wrong_pixel = np.count_nonzero(np.logical_or(actual_hits['column'] != actual_col, actual_hits['row'] != actual_row))
if n_wrong_pixel != 0:
logging.warning('%d hit(s) from other pixels for scan parameters %s', n_wrong_pixel, ', '.join(['%s=%s' % (name, value) for (name, value) in zip(scan_parameter_names, actual_scan_parameter_values)]))
actual_hits = actual_hits[np.logical_and(actual_hits['column'] == actual_col, actual_hits['row'] == actual_row)] # Only take data from selected pixel
actual_tdc_hits = actual_hits[(actual_hits['event_status'] & 0b0000111110011100) == 0b0000000100000000] # only take hits from good events (one TDC word only, no error)
actual_tot_hits = actual_hits[(actual_hits['event_status'] & 0b0000100010011100) == 0b0000000000000000] # only take hits from good events for tot
tot, tdc = actual_tot_hits['tot'], actual_tdc_hits['TDC']
if tdc.shape[0] < n_injections:
logging.info('%d of %d expected TDC hits for scan parameters %s', tdc.shape[0], n_injections, ', '.join(['%s=%s' % (name, value) for (name, value) in zip(scan_parameter_names, actual_scan_parameter_values)]))
if tot.shape[0] < n_injections:
logging.info('%d of %d expected hits for scan parameters %s', tot.shape[0], n_injections, ', '.join(['%s=%s' % (name, value) for (name, value) in zip(scan_parameter_names, actual_scan_parameter_values)]))
inner_loop_scan_parameter_index = np.where(plser_dac == inner_loop_parameter_values)[0][0] # translate the scan parameter value to an index for the result histogram
# numpy mean and std return nan if array is empty
calibration_data[actual_col - 1, actual_row - 1, inner_loop_scan_parameter_index, 0] = np.mean(tot)
calibration_data[actual_col - 1, actual_row - 1, inner_loop_scan_parameter_index, 1] = np.mean(tdc)
calibration_data[actual_col - 1, actual_row - 1, inner_loop_scan_parameter_index, 2] = np.std(tot)
calibration_data[actual_col - 1, actual_row - 1, inner_loop_scan_parameter_index, 3] = np.std(tdc)
progress_bar.update(index)
progress_bar.finish()
calibration_data_out = calibration_data_file.create_carray(calibration_data_file.root, name='HitOrCalibration', title='Hit OR calibration data', atom=tb.Atom.from_dtype(calibration_data.dtype), shape=calibration_data.shape, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
calibration_data_out[:] = calibration_data
calibration_data_out.attrs.dimensions = scan_parameter_names
calibration_data_out.attrs.scan_parameter_values = inner_loop_parameter_values
calibration_data_out.flush()
# with PdfPages(output_filename + "_calibration.pdf") as output_pdf:
plot_scurves(calibration_data[:, :, :, 0], inner_loop_parameter_values, "ToT calibration", "ToT", 15, "Charge [PlsrDAC]", filename=analyze_raw_data.output_pdf)
plot_scurves(calibration_data[:, :, :, 1], inner_loop_parameter_values, "TDC calibration", "TDC [ns]", None, "Charge [PlsrDAC]", filename=analyze_raw_data.output_pdf)
tot_mean_all_pix = np.nanmean(calibration_data[:, :, :, 0], axis=(0, 1))
tot_error_all_pix = np.nanstd(calibration_data[:, :, :, 0], axis=(0, 1))
tdc_mean_all_pix = np.nanmean(calibration_data[:, :, :, 1], axis=(0, 1))
tdc_error_all_pix = np.nanstd(calibration_data[:, :, :, 1], axis=(0, 1))
plot_tot_tdc_calibration(scan_parameters=inner_loop_parameter_values, tot_mean=tot_mean_all_pix, tot_error=tot_error_all_pix, tdc_mean=tdc_mean_all_pix, tdc_error=tdc_error_all_pix, filename=analyze_raw_data.output_pdf, title="Mean charge calibration of %d pixel(s)" % np.count_nonzero(~np.all(np.isnan(calibration_data[:, :, :, 0]), axis=2)))
# plotting individual pixels
if plot_pixel_calibrations is True:
# selecting pixels with non-nan entries
col_row_non_nan = np.nonzero(~np.all(np.isnan(calibration_data[:, :, :, 0]), axis=2))
plot_pixel_calibrations = np.dstack(col_row_non_nan)[0]
elif plot_pixel_calibrations is False:
plot_pixel_calibrations = np.array([], dtype=np.int)
else: # assuming list of column / row tuples
plot_pixel_calibrations = np.array(plot_pixel_calibrations) - 1
# generate index array
pixel_indices = np.arange(plot_pixel_calibrations.shape[0])
plot_n_pixels = 10 # number of pixels at the beginning, center and end of the array
np.random.seed(0)
# select random pixels
if pixel_indices.size - 2 * plot_n_pixels >= 0:
random_pixel_indices = np.sort(np.random.choice(pixel_indices[plot_n_pixels:-plot_n_pixels], min(plot_n_pixels, pixel_indices.size - 2 * plot_n_pixels), replace=False))
else:
random_pixel_indices = np.array([], dtype=np.int)
selected_pixel_indices = np.unique(np.hstack([pixel_indices[:plot_n_pixels], random_pixel_indices, pixel_indices[-plot_n_pixels:]]))
# plotting individual pixels
for (column, row) in plot_pixel_calibrations[selected_pixel_indices]:
logging.info("Plotting charge calibration for pixel column " + str(column + 1) + " / row " + str(row + 1))
tot_mean_single_pix = calibration_data[column, row, :, 0]
tot_std_single_pix = calibration_data[column, row, :, 2]
tdc_mean_single_pix = calibration_data[column, row, :, 1]
tdc_std_single_pix = calibration_data[column, row, :, 3]
plot_tot_tdc_calibration(scan_parameters=inner_loop_parameter_values, tot_mean=tot_mean_single_pix, tot_error=tot_std_single_pix, tdc_mean=tdc_mean_single_pix, tdc_error=tdc_std_single_pix, filename=analyze_raw_data.output_pdf, title="Charge calibration for pixel column " + str(column + 1) + " / row " + str(row + 1))
|
python
|
def create_hitor_calibration(output_filename, plot_pixel_calibrations=False):
'''Generating HitOr calibration file (_calibration.h5) from raw data file and plotting of calibration data.
Parameters
----------
output_filename : string
Input raw data file name.
plot_pixel_calibrations : bool, iterable
If True, genearating additional pixel calibration plots. If list of column and row tuples (from 1 to 80 / 336), print selected pixels.
Returns
-------
nothing
'''
logging.info('Analyze HitOR calibration data and plot results of %s', output_filename)
with AnalyzeRawData(raw_data_file=output_filename, create_pdf=True) as analyze_raw_data: # Interpret the raw data file
analyze_raw_data.create_occupancy_hist = False # too many scan parameters to do in ram histogramming
analyze_raw_data.create_hit_table = True
analyze_raw_data.create_tdc_hist = True
analyze_raw_data.align_at_tdc = True # align events at TDC words, first word of event has to be a tdc word
analyze_raw_data.interpret_word_table()
analyze_raw_data.interpreter.print_summary()
analyze_raw_data.plot_histograms()
n_injections = analyze_raw_data.n_injections # use later
meta_data = analyze_raw_data.out_file_h5.root.meta_data[:]
scan_parameters_dict = get_scan_parameter(meta_data)
inner_loop_parameter_values = scan_parameters_dict[next(reversed(scan_parameters_dict))] # inner loop parameter name is unknown
scan_parameter_names = scan_parameters_dict.keys()
# col_row_combinations = get_unique_scan_parameter_combinations(analyze_raw_data.out_file_h5.root.meta_data[:], scan_parameters=('column', 'row'), scan_parameter_columns_only=True)
meta_data_table_at_scan_parameter = get_unique_scan_parameter_combinations(meta_data, scan_parameters=scan_parameter_names)
scan_parameter_values = get_scan_parameters_table_from_meta_data(meta_data_table_at_scan_parameter, scan_parameter_names)
event_number_ranges = get_ranges_from_array(meta_data_table_at_scan_parameter['event_number'])
event_ranges_per_parameter = np.column_stack((scan_parameter_values, event_number_ranges))
if analyze_raw_data.out_file_h5.root.Hits.nrows == 0:
raise AnalysisError("Found no hits.")
hits = analyze_raw_data.out_file_h5.root.Hits[:]
event_numbers = hits['event_number'].copy() # create contigous array, otherwise np.searchsorted too slow, http://stackoverflow.com/questions/15139299/performance-of-numpy-searchsorted-is-poor-on-structured-arrays
output_filename = os.path.splitext(output_filename)[0]
with tb.open_file(output_filename + "_calibration.h5", mode="w") as calibration_data_file:
logging.info('Create calibration')
calibration_data = np.full(shape=(80, 336, len(inner_loop_parameter_values), 4), fill_value=np.nan, dtype='f4') # result of the calibration is a histogram with col_index, row_index, plsrDAC value, mean discrete tot, rms discrete tot, mean tot from TDC, rms tot from TDC
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.AdaptiveETA()], maxval=len(event_ranges_per_parameter), term_width=80)
progress_bar.start()
for index, (actual_scan_parameter_values, event_start, event_stop) in enumerate(event_ranges_per_parameter):
if event_stop is None: # happens for the last chunk
event_stop = hits[-1]['event_number'] + 1
array_index = np.searchsorted(event_numbers, np.array([event_start, event_stop]))
actual_hits = hits[array_index[0]:array_index[1]]
for item_index, item in enumerate(scan_parameter_names):
if item == "column":
actual_col = actual_scan_parameter_values[item_index]
elif item == "row":
actual_row = actual_scan_parameter_values[item_index]
elif item == "PlsrDAC":
plser_dac = actual_scan_parameter_values[item_index]
else:
raise ValueError("Unknown scan parameter %s" % item)
# Only pixel of actual column/row should be in the actual data chunk but since FIFO is not cleared for each scan step due to speed reasons and there might be noisy pixels this is not always the case
n_wrong_pixel = np.count_nonzero(np.logical_or(actual_hits['column'] != actual_col, actual_hits['row'] != actual_row))
if n_wrong_pixel != 0:
logging.warning('%d hit(s) from other pixels for scan parameters %s', n_wrong_pixel, ', '.join(['%s=%s' % (name, value) for (name, value) in zip(scan_parameter_names, actual_scan_parameter_values)]))
actual_hits = actual_hits[np.logical_and(actual_hits['column'] == actual_col, actual_hits['row'] == actual_row)] # Only take data from selected pixel
actual_tdc_hits = actual_hits[(actual_hits['event_status'] & 0b0000111110011100) == 0b0000000100000000] # only take hits from good events (one TDC word only, no error)
actual_tot_hits = actual_hits[(actual_hits['event_status'] & 0b0000100010011100) == 0b0000000000000000] # only take hits from good events for tot
tot, tdc = actual_tot_hits['tot'], actual_tdc_hits['TDC']
if tdc.shape[0] < n_injections:
logging.info('%d of %d expected TDC hits for scan parameters %s', tdc.shape[0], n_injections, ', '.join(['%s=%s' % (name, value) for (name, value) in zip(scan_parameter_names, actual_scan_parameter_values)]))
if tot.shape[0] < n_injections:
logging.info('%d of %d expected hits for scan parameters %s', tot.shape[0], n_injections, ', '.join(['%s=%s' % (name, value) for (name, value) in zip(scan_parameter_names, actual_scan_parameter_values)]))
inner_loop_scan_parameter_index = np.where(plser_dac == inner_loop_parameter_values)[0][0] # translate the scan parameter value to an index for the result histogram
# numpy mean and std return nan if array is empty
calibration_data[actual_col - 1, actual_row - 1, inner_loop_scan_parameter_index, 0] = np.mean(tot)
calibration_data[actual_col - 1, actual_row - 1, inner_loop_scan_parameter_index, 1] = np.mean(tdc)
calibration_data[actual_col - 1, actual_row - 1, inner_loop_scan_parameter_index, 2] = np.std(tot)
calibration_data[actual_col - 1, actual_row - 1, inner_loop_scan_parameter_index, 3] = np.std(tdc)
progress_bar.update(index)
progress_bar.finish()
calibration_data_out = calibration_data_file.create_carray(calibration_data_file.root, name='HitOrCalibration', title='Hit OR calibration data', atom=tb.Atom.from_dtype(calibration_data.dtype), shape=calibration_data.shape, filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
calibration_data_out[:] = calibration_data
calibration_data_out.attrs.dimensions = scan_parameter_names
calibration_data_out.attrs.scan_parameter_values = inner_loop_parameter_values
calibration_data_out.flush()
# with PdfPages(output_filename + "_calibration.pdf") as output_pdf:
plot_scurves(calibration_data[:, :, :, 0], inner_loop_parameter_values, "ToT calibration", "ToT", 15, "Charge [PlsrDAC]", filename=analyze_raw_data.output_pdf)
plot_scurves(calibration_data[:, :, :, 1], inner_loop_parameter_values, "TDC calibration", "TDC [ns]", None, "Charge [PlsrDAC]", filename=analyze_raw_data.output_pdf)
tot_mean_all_pix = np.nanmean(calibration_data[:, :, :, 0], axis=(0, 1))
tot_error_all_pix = np.nanstd(calibration_data[:, :, :, 0], axis=(0, 1))
tdc_mean_all_pix = np.nanmean(calibration_data[:, :, :, 1], axis=(0, 1))
tdc_error_all_pix = np.nanstd(calibration_data[:, :, :, 1], axis=(0, 1))
plot_tot_tdc_calibration(scan_parameters=inner_loop_parameter_values, tot_mean=tot_mean_all_pix, tot_error=tot_error_all_pix, tdc_mean=tdc_mean_all_pix, tdc_error=tdc_error_all_pix, filename=analyze_raw_data.output_pdf, title="Mean charge calibration of %d pixel(s)" % np.count_nonzero(~np.all(np.isnan(calibration_data[:, :, :, 0]), axis=2)))
# plotting individual pixels
if plot_pixel_calibrations is True:
# selecting pixels with non-nan entries
col_row_non_nan = np.nonzero(~np.all(np.isnan(calibration_data[:, :, :, 0]), axis=2))
plot_pixel_calibrations = np.dstack(col_row_non_nan)[0]
elif plot_pixel_calibrations is False:
plot_pixel_calibrations = np.array([], dtype=np.int)
else: # assuming list of column / row tuples
plot_pixel_calibrations = np.array(plot_pixel_calibrations) - 1
# generate index array
pixel_indices = np.arange(plot_pixel_calibrations.shape[0])
plot_n_pixels = 10 # number of pixels at the beginning, center and end of the array
np.random.seed(0)
# select random pixels
if pixel_indices.size - 2 * plot_n_pixels >= 0:
random_pixel_indices = np.sort(np.random.choice(pixel_indices[plot_n_pixels:-plot_n_pixels], min(plot_n_pixels, pixel_indices.size - 2 * plot_n_pixels), replace=False))
else:
random_pixel_indices = np.array([], dtype=np.int)
selected_pixel_indices = np.unique(np.hstack([pixel_indices[:plot_n_pixels], random_pixel_indices, pixel_indices[-plot_n_pixels:]]))
# plotting individual pixels
for (column, row) in plot_pixel_calibrations[selected_pixel_indices]:
logging.info("Plotting charge calibration for pixel column " + str(column + 1) + " / row " + str(row + 1))
tot_mean_single_pix = calibration_data[column, row, :, 0]
tot_std_single_pix = calibration_data[column, row, :, 2]
tdc_mean_single_pix = calibration_data[column, row, :, 1]
tdc_std_single_pix = calibration_data[column, row, :, 3]
plot_tot_tdc_calibration(scan_parameters=inner_loop_parameter_values, tot_mean=tot_mean_single_pix, tot_error=tot_std_single_pix, tdc_mean=tdc_mean_single_pix, tdc_error=tdc_std_single_pix, filename=analyze_raw_data.output_pdf, title="Charge calibration for pixel column " + str(column + 1) + " / row " + str(row + 1))
|
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] |
Generating HitOr calibration file (_calibration.h5) from raw data file and plotting of calibration data.
Parameters
----------
output_filename : string
Input raw data file name.
plot_pixel_calibrations : bool, iterable
If True, genearating additional pixel calibration plots. If list of column and row tuples (from 1 to 80 / 336), print selected pixels.
Returns
-------
nothing
|
[
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"Returns",
"-------",
"nothing"
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/scans/calibrate_hit_or.py#L20-L148
|
SiLab-Bonn/pyBAR
|
pybar/fei4_run_base.py
|
interval_timed
|
def interval_timed(interval):
'''Interval timer decorator.
Taken from: http://stackoverflow.com/questions/12435211/python-threading-timer-repeat-function-every-n-seconds/12435256
'''
def decorator(f):
@wraps(f)
def wrapper(*args, **kwargs):
stopped = Event()
def loop(): # executed in another thread
while not stopped.wait(interval): # until stopped
f(*args, **kwargs)
t = Thread(name='IntervalTimerThread', target=loop)
t.daemon = True # stop if the program exits
t.start()
return stopped.set
return wrapper
return decorator
|
python
|
def interval_timed(interval):
'''Interval timer decorator.
Taken from: http://stackoverflow.com/questions/12435211/python-threading-timer-repeat-function-every-n-seconds/12435256
'''
def decorator(f):
@wraps(f)
def wrapper(*args, **kwargs):
stopped = Event()
def loop(): # executed in another thread
while not stopped.wait(interval): # until stopped
f(*args, **kwargs)
t = Thread(name='IntervalTimerThread', target=loop)
t.daemon = True # stop if the program exits
t.start()
return stopped.set
return wrapper
return decorator
|
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] |
Interval timer decorator.
Taken from: http://stackoverflow.com/questions/12435211/python-threading-timer-repeat-function-every-n-seconds/12435256
|
[
"Interval",
"timer",
"decorator",
"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4_run_base.py#L1423-L1442
|
SiLab-Bonn/pyBAR
|
pybar/fei4_run_base.py
|
interval_timer
|
def interval_timer(interval, func, *args, **kwargs):
'''Interval timer function.
Taken from: http://stackoverflow.com/questions/22498038/improvement-on-interval-python/22498708
'''
stopped = Event()
def loop():
while not stopped.wait(interval): # the first call is after interval
func(*args, **kwargs)
Thread(name='IntervalTimerThread', target=loop).start()
return stopped.set
|
python
|
def interval_timer(interval, func, *args, **kwargs):
'''Interval timer function.
Taken from: http://stackoverflow.com/questions/22498038/improvement-on-interval-python/22498708
'''
stopped = Event()
def loop():
while not stopped.wait(interval): # the first call is after interval
func(*args, **kwargs)
Thread(name='IntervalTimerThread', target=loop).start()
return stopped.set
|
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Interval timer function.
Taken from: http://stackoverflow.com/questions/22498038/improvement-on-interval-python/22498708
|
[
"Interval",
"timer",
"function",
"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4_run_base.py#L1445-L1457
|
SiLab-Bonn/pyBAR
|
pybar/fei4_run_base.py
|
send_mail
|
def send_mail(subject, body, smtp_server, user, password, from_addr, to_addrs):
''' Sends a run status mail with the traceback to a specified E-Mail address if a run crashes.
'''
logging.info('Send status E-Mail (' + subject + ')')
content = string.join((
"From: %s" % from_addr,
"To: %s" % ','.join(to_addrs), # comma separated according to RFC822
"Subject: %s" % subject,
"",
body),
"\r\n")
server = smtplib.SMTP_SSL(smtp_server)
server.login(user, password)
server.sendmail(from_addr, to_addrs, content)
server.quit()
|
python
|
def send_mail(subject, body, smtp_server, user, password, from_addr, to_addrs):
''' Sends a run status mail with the traceback to a specified E-Mail address if a run crashes.
'''
logging.info('Send status E-Mail (' + subject + ')')
content = string.join((
"From: %s" % from_addr,
"To: %s" % ','.join(to_addrs), # comma separated according to RFC822
"Subject: %s" % subject,
"",
body),
"\r\n")
server = smtplib.SMTP_SSL(smtp_server)
server.login(user, password)
server.sendmail(from_addr, to_addrs, content)
server.quit()
|
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|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4_run_base.py#L1493-L1507
|
SiLab-Bonn/pyBAR
|
pybar/fei4_run_base.py
|
Fei4RunBase._parse_module_cfgs
|
def _parse_module_cfgs(self):
''' Extracts the configuration of the modules.
'''
# Adding here default run config parameters.
if "dut" not in self._conf or self._conf["dut"] is None:
raise ValueError('Parameter "dut" not defined.')
if "dut_configuration" not in self._conf or self._conf["dut_configuration"] is None:
raise ValueError('Parameter "dut_configuration" not defined.')
self._conf.setdefault('working_dir', None) # string, if None, absolute path of configuration.yaml file will be used
if 'modules' in self._conf and self._conf['modules']:
for module_id, module_cfg in [(key, value) for key, value in self._conf['modules'].items() if ("activate" not in value or ("activate" in value and value["activate"] is True))]:
# Check here for missing module config items.
# Capital letter keys are Basil drivers, other keys are parameters.
# FIFO, RX, TX, TLU and TDC are generic driver names which are used in the scan implementations.
# The use of these reserved driver names allows for abstraction.
# Accessing Basil drivers with real name is still possible.
if "module_group" in module_id:
raise ValueError('The module ID "%s" contains the reserved name "module_group".' % module_id)
if "flavor" not in module_cfg or module_cfg["flavor"] is None:
raise ValueError('No parameter "flavor" defined for module "%s".' % module_id)
if module_cfg["flavor"] in fe_flavors:
for driver_name in _reserved_driver_names:
# TDC is not mandatory
if driver_name == "TDC":
# TDC is allowed to have set None
module_cfg.setdefault('TDC', None)
continue
if driver_name not in module_cfg or module_cfg[driver_name] is None:
raise ValueError('No parameter "%s" defined for module "%s".' % (driver_name, module_id))
if "rx_channel" not in module_cfg or module_cfg["rx_channel"] is None:
raise ValueError('No parameter "rx_channel" defined for module "%s".' % module_id)
if "tx_channel" not in module_cfg or module_cfg["tx_channel"] is None:
raise ValueError('No parameter "tx_channel" defined for module "%s".' % module_id)
if "chip_address" not in module_cfg:
raise ValueError('No parameter "chip_address" defined for module "%s".' % module_id)
module_cfg.setdefault("tdc_channel", None)
module_cfg.setdefault("configuration", None) # string or number, if None, using the last valid configuration
module_cfg.setdefault("send_data", None) # address string of PUB socket
module_cfg.setdefault("activate", True) # set module active by default
# Save config to dict.
self._module_cfgs[module_id] = module_cfg
self._modules[module_id] = [module_id]
else:
raise ValueError("No module configuration specified")
|
python
|
def _parse_module_cfgs(self):
''' Extracts the configuration of the modules.
'''
# Adding here default run config parameters.
if "dut" not in self._conf or self._conf["dut"] is None:
raise ValueError('Parameter "dut" not defined.')
if "dut_configuration" not in self._conf or self._conf["dut_configuration"] is None:
raise ValueError('Parameter "dut_configuration" not defined.')
self._conf.setdefault('working_dir', None) # string, if None, absolute path of configuration.yaml file will be used
if 'modules' in self._conf and self._conf['modules']:
for module_id, module_cfg in [(key, value) for key, value in self._conf['modules'].items() if ("activate" not in value or ("activate" in value and value["activate"] is True))]:
# Check here for missing module config items.
# Capital letter keys are Basil drivers, other keys are parameters.
# FIFO, RX, TX, TLU and TDC are generic driver names which are used in the scan implementations.
# The use of these reserved driver names allows for abstraction.
# Accessing Basil drivers with real name is still possible.
if "module_group" in module_id:
raise ValueError('The module ID "%s" contains the reserved name "module_group".' % module_id)
if "flavor" not in module_cfg or module_cfg["flavor"] is None:
raise ValueError('No parameter "flavor" defined for module "%s".' % module_id)
if module_cfg["flavor"] in fe_flavors:
for driver_name in _reserved_driver_names:
# TDC is not mandatory
if driver_name == "TDC":
# TDC is allowed to have set None
module_cfg.setdefault('TDC', None)
continue
if driver_name not in module_cfg or module_cfg[driver_name] is None:
raise ValueError('No parameter "%s" defined for module "%s".' % (driver_name, module_id))
if "rx_channel" not in module_cfg or module_cfg["rx_channel"] is None:
raise ValueError('No parameter "rx_channel" defined for module "%s".' % module_id)
if "tx_channel" not in module_cfg or module_cfg["tx_channel"] is None:
raise ValueError('No parameter "tx_channel" defined for module "%s".' % module_id)
if "chip_address" not in module_cfg:
raise ValueError('No parameter "chip_address" defined for module "%s".' % module_id)
module_cfg.setdefault("tdc_channel", None)
module_cfg.setdefault("configuration", None) # string or number, if None, using the last valid configuration
module_cfg.setdefault("send_data", None) # address string of PUB socket
module_cfg.setdefault("activate", True) # set module active by default
# Save config to dict.
self._module_cfgs[module_id] = module_cfg
self._modules[module_id] = [module_id]
else:
raise ValueError("No module configuration specified")
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4_run_base.py#L175-L219
|
SiLab-Bonn/pyBAR
|
pybar/fei4_run_base.py
|
Fei4RunBase._set_default_cfg
|
def _set_default_cfg(self):
''' Sets the default parameters if they are not specified.
'''
# adding special conf for accessing all DUT drivers
self._module_cfgs[None] = {
'flavor': None,
'chip_address': None,
'FIFO': list(set([self._module_cfgs[module_id]['FIFO'] for module_id in self._modules])),
'RX': list(set([self._module_cfgs[module_id]['RX'] for module_id in self._modules])),
'rx_channel': list(set([self._module_cfgs[module_id]['rx_channel'] for module_id in self._modules])),
'TX': list(set([self._module_cfgs[module_id]['TX'] for module_id in self._modules])),
'tx_channel': list(set([self._module_cfgs[module_id]['tx_channel'] for module_id in self._modules])),
'TDC': list(set([self._module_cfgs[module_id]['TDC'] for module_id in self._modules])),
'tdc_channel': list(set([self._module_cfgs[module_id]['tdc_channel'] for module_id in self._modules])),
'TLU': list(set([self._module_cfgs[module_id]['TLU'] for module_id in self._modules])),
'configuration': None,
'send_data': None}
tx_groups = groupby_dict({key: value for (key, value) in self._module_cfgs.items() if key in self._modules}, "TX")
for tx, module_group in tx_groups.items():
flavors = list(set([module_cfg['flavor'] for module_id, module_cfg in self._module_cfgs.items() if module_id in module_group]))
if len(flavors) != 1:
raise ValueError("Parameter 'flavor' must be the same for module group TX=%s." % tx)
chip_addresses = list(set([module_cfg['chip_address'] for module_id, module_cfg in self._module_cfgs.items() if module_id in module_group]))
if len(module_group) != len(chip_addresses) or (len(module_group) != 1 and None in chip_addresses):
raise ValueError("Parameter 'chip_address' must be different for each module in module group TX=%s." % tx)
# Adding broadcast config for parallel mode.
self._module_cfgs["module_group_TX=" + tx] = {
'flavor': flavors[0],
'chip_address': None, # broadcast
'FIFO': list(set([module_cfg['FIFO'] for module_id, module_cfg in self._module_cfgs.items() if module_id in module_group])),
'RX': list(set([module_cfg['RX'] for module_id, module_cfg in self._module_cfgs.items() if module_id in module_group])),
'rx_channel': list(set([module_cfg['rx_channel'] for module_id, module_cfg in self._module_cfgs.items() if module_id in module_group])),
'TX': tx,
'tx_channel': list(set([module_cfg['tx_channel'] for module_id, module_cfg in self._module_cfgs.items() if module_id in module_group])),
'TDC': list(set([module_cfg['TDC'] for module_id, module_cfg in self._module_cfgs.items() if module_id in module_group])),
'tdc_channel': list(set([module_cfg['tdc_channel'] for module_id, module_cfg in self._module_cfgs.items() if module_id in module_group])),
'TLU': list(set([module_cfg['TLU'] for module_id, module_cfg in self._module_cfgs.items() if module_id in module_group])),
'configuration': None,
'send_data': None}
self._tx_module_groups["module_group_TX=" + tx] = module_group
# Setting up per module attributes
self._module_attr = {key: {} for key in self._module_cfgs}
# Setting up per module run conf
for module_id in self._module_cfgs:
sc = namedtuple('run_configuration', field_names=self._default_run_conf.keys())
run_conf = sc(**self._run_conf)
if module_id in self._modules and self.__class__.__name__ in self._conf["modules"][module_id] and self._conf["modules"][module_id][self.__class__.__name__] is not None:
self._module_run_conf[module_id] = run_conf._replace(**self._conf["modules"][module_id][self.__class__.__name__])._asdict()
else:
self._module_run_conf[module_id] = run_conf._asdict()
# update module group with run specific configuration
if module_id in self._tx_module_groups and self._tx_module_groups[module_id]:
selected_module_id = self._tx_module_groups[module_id][0]
if self.__class__.__name__ in self._conf["modules"][selected_module_id] and self._conf["modules"][selected_module_id][self.__class__.__name__] is not None:
self._module_run_conf[module_id] = run_conf._replace(**self._conf["modules"][selected_module_id][self.__class__.__name__])._asdict()
|
python
|
def _set_default_cfg(self):
''' Sets the default parameters if they are not specified.
'''
# adding special conf for accessing all DUT drivers
self._module_cfgs[None] = {
'flavor': None,
'chip_address': None,
'FIFO': list(set([self._module_cfgs[module_id]['FIFO'] for module_id in self._modules])),
'RX': list(set([self._module_cfgs[module_id]['RX'] for module_id in self._modules])),
'rx_channel': list(set([self._module_cfgs[module_id]['rx_channel'] for module_id in self._modules])),
'TX': list(set([self._module_cfgs[module_id]['TX'] for module_id in self._modules])),
'tx_channel': list(set([self._module_cfgs[module_id]['tx_channel'] for module_id in self._modules])),
'TDC': list(set([self._module_cfgs[module_id]['TDC'] for module_id in self._modules])),
'tdc_channel': list(set([self._module_cfgs[module_id]['tdc_channel'] for module_id in self._modules])),
'TLU': list(set([self._module_cfgs[module_id]['TLU'] for module_id in self._modules])),
'configuration': None,
'send_data': None}
tx_groups = groupby_dict({key: value for (key, value) in self._module_cfgs.items() if key in self._modules}, "TX")
for tx, module_group in tx_groups.items():
flavors = list(set([module_cfg['flavor'] for module_id, module_cfg in self._module_cfgs.items() if module_id in module_group]))
if len(flavors) != 1:
raise ValueError("Parameter 'flavor' must be the same for module group TX=%s." % tx)
chip_addresses = list(set([module_cfg['chip_address'] for module_id, module_cfg in self._module_cfgs.items() if module_id in module_group]))
if len(module_group) != len(chip_addresses) or (len(module_group) != 1 and None in chip_addresses):
raise ValueError("Parameter 'chip_address' must be different for each module in module group TX=%s." % tx)
# Adding broadcast config for parallel mode.
self._module_cfgs["module_group_TX=" + tx] = {
'flavor': flavors[0],
'chip_address': None, # broadcast
'FIFO': list(set([module_cfg['FIFO'] for module_id, module_cfg in self._module_cfgs.items() if module_id in module_group])),
'RX': list(set([module_cfg['RX'] for module_id, module_cfg in self._module_cfgs.items() if module_id in module_group])),
'rx_channel': list(set([module_cfg['rx_channel'] for module_id, module_cfg in self._module_cfgs.items() if module_id in module_group])),
'TX': tx,
'tx_channel': list(set([module_cfg['tx_channel'] for module_id, module_cfg in self._module_cfgs.items() if module_id in module_group])),
'TDC': list(set([module_cfg['TDC'] for module_id, module_cfg in self._module_cfgs.items() if module_id in module_group])),
'tdc_channel': list(set([module_cfg['tdc_channel'] for module_id, module_cfg in self._module_cfgs.items() if module_id in module_group])),
'TLU': list(set([module_cfg['TLU'] for module_id, module_cfg in self._module_cfgs.items() if module_id in module_group])),
'configuration': None,
'send_data': None}
self._tx_module_groups["module_group_TX=" + tx] = module_group
# Setting up per module attributes
self._module_attr = {key: {} for key in self._module_cfgs}
# Setting up per module run conf
for module_id in self._module_cfgs:
sc = namedtuple('run_configuration', field_names=self._default_run_conf.keys())
run_conf = sc(**self._run_conf)
if module_id in self._modules and self.__class__.__name__ in self._conf["modules"][module_id] and self._conf["modules"][module_id][self.__class__.__name__] is not None:
self._module_run_conf[module_id] = run_conf._replace(**self._conf["modules"][module_id][self.__class__.__name__])._asdict()
else:
self._module_run_conf[module_id] = run_conf._asdict()
# update module group with run specific configuration
if module_id in self._tx_module_groups and self._tx_module_groups[module_id]:
selected_module_id = self._tx_module_groups[module_id][0]
if self.__class__.__name__ in self._conf["modules"][selected_module_id] and self._conf["modules"][selected_module_id][self.__class__.__name__] is not None:
self._module_run_conf[module_id] = run_conf._replace(**self._conf["modules"][selected_module_id][self.__class__.__name__])._asdict()
|
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Sets the default parameters if they are not specified.
|
[
"Sets",
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] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4_run_base.py#L265-L323
|
SiLab-Bonn/pyBAR
|
pybar/fei4_run_base.py
|
Fei4RunBase.init_modules
|
def init_modules(self):
''' Initialize all modules consecutively'''
for module_id, module_cfg in self._module_cfgs.items():
if module_id in self._modules or module_id in self._tx_module_groups:
if module_id in self._modules:
module_id_str = "module " + module_id
else:
module_id_str = module_id.split('=', 1)
module_id_str[0] = module_id_str[0].replace("_", " ")
module_id_str = "=".join(module_id_str)
logging.info("Initializing configuration for %s..." % module_id_str)
# adding scan parameters to dict
if 'scan_parameters' in self._module_run_conf[module_id] and self._module_run_conf[module_id]['scan_parameters'] is not None:
# evaluating string for support of nested lists and other complex data structures
if isinstance(self._module_run_conf[module_id]['scan_parameters'], basestring):
self._module_run_conf[module_id]['scan_parameters'] = ast.literal_eval(self._module_run_conf[module_id]['scan_parameters'])
sp = namedtuple('scan_parameters', field_names=zip(*self._module_run_conf[module_id]['scan_parameters'])[0])
self._scan_parameters[module_id] = sp(*zip(*self._module_run_conf[module_id]['scan_parameters'])[1])
else:
sp = namedtuple_with_defaults('scan_parameters', field_names=[])
self._scan_parameters[module_id] = sp()
# init FE config
if module_id in self._modules:
# only real modules can have an existing configuration
last_configuration = self.get_configuration(module_id=module_id)
else:
last_configuration = None
if (('configuration' not in module_cfg or module_cfg['configuration'] is None) and last_configuration is None) or (isinstance(module_cfg['configuration'], (int, long)) and module_cfg['configuration'] <= 0):
if 'chip_address' in module_cfg:
if module_cfg['chip_address'] is None:
chip_address = 0
broadcast = True
else:
chip_address = module_cfg['chip_address']
broadcast = False
else:
raise ValueError('Parameter "chip_address" not specified for module "%s".' % module_id)
if 'flavor' in module_cfg and module_cfg['flavor']:
module_cfg['configuration'] = FEI4Register(fe_type=module_cfg['flavor'], chip_address=chip_address, broadcast=broadcast)
else:
raise ValueError('Parameter "flavor" not specified for module "%s".' % module_id)
# use existing config
elif not module_cfg['configuration'] and last_configuration:
module_cfg['configuration'] = FEI4Register(configuration_file=last_configuration)
# path string
elif isinstance(module_cfg['configuration'], basestring):
if os.path.isabs(module_cfg['configuration']): # absolute path
module_cfg['configuration'] = FEI4Register(configuration_file=module_cfg['configuration'])
else: # relative path
module_cfg['configuration'] = FEI4Register(configuration_file=os.path.join(module_cfg['working_dir'], module_cfg['configuration']))
# run number
elif isinstance(module_cfg['configuration'], (int, long)) and module_cfg['configuration'] > 0:
module_cfg['configuration'] = FEI4Register(configuration_file=self.get_configuration(module_id=module_id, run_number=module_cfg['configuration']))
# assume configuration already initialized
elif not isinstance(module_cfg['configuration'], FEI4Register):
raise ValueError('Found no valid value for parameter "configuration" for module "%s".' % module_id)
# init register utils
self._registers[module_id] = self._module_cfgs[module_id]['configuration']
self._register_utils[module_id] = FEI4RegisterUtils(self._module_dut[module_id], self._module_cfgs[module_id]['configuration'])
if module_id in self._modules:
# Create module data path for real modules
module_path = self.get_module_path(module_id)
if not os.path.exists(module_path):
os.makedirs(module_path)
# Set all modules to conf mode to prevent from receiving BCR and ECR broadcast
for module_id in self._tx_module_groups:
with self.access_module(module_id=module_id):
self.register_utils.set_conf_mode()
# Initial configuration (reset and configuration) of all modules.
# This is done by iterating over each module individually
for module_id in self._modules:
logging.info("Configuring %s..." % module_id)
with self.access_module(module_id=module_id):
if self._run_conf['configure_fe']:
self.register_utils.global_reset()
self.register_utils.configure_all()
else:
self.register_utils.set_conf_mode()
if is_fe_ready(self):
fe_not_ready = False
else:
fe_not_ready = True
# BCR and ECR might result in RX errors
# a reset of the RX and FIFO will happen just before scan()
if self._run_conf['reset_fe']:
self.register_utils.reset_bunch_counter()
self.register_utils.reset_event_counter()
if fe_not_ready:
# resetting service records must be done once after power up
self.register_utils.reset_service_records()
if not is_fe_ready(self):
logging.warning('Module "%s" is not sending any data.' % module_id)
# set all modules to conf mode afterwards to be immune to ECR and BCR
self.register_utils.set_conf_mode()
|
python
|
def init_modules(self):
''' Initialize all modules consecutively'''
for module_id, module_cfg in self._module_cfgs.items():
if module_id in self._modules or module_id in self._tx_module_groups:
if module_id in self._modules:
module_id_str = "module " + module_id
else:
module_id_str = module_id.split('=', 1)
module_id_str[0] = module_id_str[0].replace("_", " ")
module_id_str = "=".join(module_id_str)
logging.info("Initializing configuration for %s..." % module_id_str)
# adding scan parameters to dict
if 'scan_parameters' in self._module_run_conf[module_id] and self._module_run_conf[module_id]['scan_parameters'] is not None:
# evaluating string for support of nested lists and other complex data structures
if isinstance(self._module_run_conf[module_id]['scan_parameters'], basestring):
self._module_run_conf[module_id]['scan_parameters'] = ast.literal_eval(self._module_run_conf[module_id]['scan_parameters'])
sp = namedtuple('scan_parameters', field_names=zip(*self._module_run_conf[module_id]['scan_parameters'])[0])
self._scan_parameters[module_id] = sp(*zip(*self._module_run_conf[module_id]['scan_parameters'])[1])
else:
sp = namedtuple_with_defaults('scan_parameters', field_names=[])
self._scan_parameters[module_id] = sp()
# init FE config
if module_id in self._modules:
# only real modules can have an existing configuration
last_configuration = self.get_configuration(module_id=module_id)
else:
last_configuration = None
if (('configuration' not in module_cfg or module_cfg['configuration'] is None) and last_configuration is None) or (isinstance(module_cfg['configuration'], (int, long)) and module_cfg['configuration'] <= 0):
if 'chip_address' in module_cfg:
if module_cfg['chip_address'] is None:
chip_address = 0
broadcast = True
else:
chip_address = module_cfg['chip_address']
broadcast = False
else:
raise ValueError('Parameter "chip_address" not specified for module "%s".' % module_id)
if 'flavor' in module_cfg and module_cfg['flavor']:
module_cfg['configuration'] = FEI4Register(fe_type=module_cfg['flavor'], chip_address=chip_address, broadcast=broadcast)
else:
raise ValueError('Parameter "flavor" not specified for module "%s".' % module_id)
# use existing config
elif not module_cfg['configuration'] and last_configuration:
module_cfg['configuration'] = FEI4Register(configuration_file=last_configuration)
# path string
elif isinstance(module_cfg['configuration'], basestring):
if os.path.isabs(module_cfg['configuration']): # absolute path
module_cfg['configuration'] = FEI4Register(configuration_file=module_cfg['configuration'])
else: # relative path
module_cfg['configuration'] = FEI4Register(configuration_file=os.path.join(module_cfg['working_dir'], module_cfg['configuration']))
# run number
elif isinstance(module_cfg['configuration'], (int, long)) and module_cfg['configuration'] > 0:
module_cfg['configuration'] = FEI4Register(configuration_file=self.get_configuration(module_id=module_id, run_number=module_cfg['configuration']))
# assume configuration already initialized
elif not isinstance(module_cfg['configuration'], FEI4Register):
raise ValueError('Found no valid value for parameter "configuration" for module "%s".' % module_id)
# init register utils
self._registers[module_id] = self._module_cfgs[module_id]['configuration']
self._register_utils[module_id] = FEI4RegisterUtils(self._module_dut[module_id], self._module_cfgs[module_id]['configuration'])
if module_id in self._modules:
# Create module data path for real modules
module_path = self.get_module_path(module_id)
if not os.path.exists(module_path):
os.makedirs(module_path)
# Set all modules to conf mode to prevent from receiving BCR and ECR broadcast
for module_id in self._tx_module_groups:
with self.access_module(module_id=module_id):
self.register_utils.set_conf_mode()
# Initial configuration (reset and configuration) of all modules.
# This is done by iterating over each module individually
for module_id in self._modules:
logging.info("Configuring %s..." % module_id)
with self.access_module(module_id=module_id):
if self._run_conf['configure_fe']:
self.register_utils.global_reset()
self.register_utils.configure_all()
else:
self.register_utils.set_conf_mode()
if is_fe_ready(self):
fe_not_ready = False
else:
fe_not_ready = True
# BCR and ECR might result in RX errors
# a reset of the RX and FIFO will happen just before scan()
if self._run_conf['reset_fe']:
self.register_utils.reset_bunch_counter()
self.register_utils.reset_event_counter()
if fe_not_ready:
# resetting service records must be done once after power up
self.register_utils.reset_service_records()
if not is_fe_ready(self):
logging.warning('Module "%s" is not sending any data.' % module_id)
# set all modules to conf mode afterwards to be immune to ECR and BCR
self.register_utils.set_conf_mode()
|
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] |
Initialize all modules consecutively
|
[
"Initialize",
"all",
"modules",
"consecutively"
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4_run_base.py#L466-L563
|
SiLab-Bonn/pyBAR
|
pybar/fei4_run_base.py
|
Fei4RunBase.do_run
|
def do_run(self):
''' Start runs on all modules sequentially.
Sets properties to access current module properties.
'''
if self.broadcast_commands: # Broadcast FE commands
if self.threaded_scan:
with ExitStack() as restore_config_stack:
# Configure each FE individually
# Sort module config keys, configure broadcast modules first
for module_id in itertools.chain(self._tx_module_groups, self._modules):
if self.abort_run.is_set():
break
with self.access_module(module_id=module_id):
if module_id in self._modules:
module_id_str = "module " + module_id
else:
module_id_str = module_id.split('=', 1)
module_id_str[0] = module_id_str[0].replace("_", " ")
module_id_str = "=".join(module_id_str)
logging.info('Scan parameter(s) for %s: %s', module_id_str, ', '.join(['%s=%s' % (key, value) for (key, value) in self.scan_parameters._asdict().items()]) if self.scan_parameters else 'None')
# storing register values until scan has finished and then restore configuration
restore_config_stack.enter_context(self.register.restored(name=self.run_number))
self.configure()
for module_id in self._tx_module_groups:
if self.abort_run.is_set():
break
with self.access_module(module_id=module_id):
# set all modules to run mode by before entering scan()
self.register_utils.set_run_mode()
with self.access_module(module_id=None):
self.fifo_readout.reset_rx()
self.fifo_readout.reset_fifo(self._selected_fifos)
self.fifo_readout.print_fei4_rx_status()
with self.access_files():
self._scan_threads = []
for module_id in self._tx_module_groups:
if self.abort_run.is_set():
break
t = ExcThread(target=self.scan, name=module_id)
t.daemon = True # exiting program even when thread is alive
self._scan_threads.append(t)
for t in self._scan_threads:
t.start()
while any([t.is_alive() for t in self._scan_threads]):
# if self.abort_run.is_set():
# break
for t in self._scan_threads:
try:
t.join(0.01)
except Exception:
self._scan_threads.remove(t)
self.handle_err(sys.exc_info())
# alive_threads = [t.name for t in self._scan_threads if (not t.join(10.0) and t.is_alive())]
# if alive_threads:
# raise RuntimeError("Scan thread(s) not finished: %s" % ", ".join(alive_threads))
self._scan_threads = []
for module_id in self._tx_module_groups:
if self.abort_run.is_set():
break
with self.access_module(module_id=module_id):
# set modules to conf mode by after finishing scan()
self.register_utils.set_conf_mode()
else:
for tx_module_id, tx_group in self._tx_module_groups.items():
if self.abort_run.is_set():
break
with ExitStack() as restore_config_stack:
for module_id in itertools.chain([tx_module_id], tx_group):
if self.abort_run.is_set():
break
with self.access_module(module_id=module_id):
logging.info('Scan parameter(s) for module %s: %s', module_id, ', '.join(['%s=%s' % (key, value) for (key, value) in self.scan_parameters._asdict().items()]) if self.scan_parameters else 'None')
# storing register values until scan has finished and then restore configuration
restore_config_stack.enter_context(self.register.restored(name=self.run_number))
self.configure()
with self.access_module(module_id=tx_module_id):
# set all modules to run mode by before entering scan()
self.register_utils.set_run_mode()
self.fifo_readout.reset_rx()
self.fifo_readout.reset_fifo(self._selected_fifos)
self.fifo_readout.print_fei4_rx_status()
# some scans use this event to stop scan loop, clear event here to make another scan possible
self.stop_run.clear()
with self.access_files():
self.scan()
with self.access_module(module_id=tx_module_id):
# set modules to conf mode by after finishing scan()
self.register_utils.set_conf_mode()
else: # Scan each FE individually
if self.threaded_scan:
self._scan_threads = []
# loop over grpups of modules with different TX
for tx_module_ids in zip_nofill(*self._tx_module_groups.values()):
if self.abort_run.is_set():
break
with ExitStack() as restore_config_stack:
for module_id in tx_module_ids:
if self.abort_run.is_set():
break
with self.access_module(module_id=module_id):
logging.info('Scan parameter(s) for module %s: %s', module_id, ', '.join(['%s=%s' % (key, value) for (key, value) in self.scan_parameters._asdict().items()]) if self.scan_parameters else 'None')
# storing register values until scan has finished and then restore configuration
restore_config_stack.enter_context(self.register.restored(name=self.run_number))
self.configure()
# set modules to run mode by before entering scan()
self.register_utils.set_run_mode()
t = ExcThread(target=self.scan, name=module_id)
t.daemon = True # exiting program even when thread is alive
self._scan_threads.append(t)
with self.access_module(module_id=tx_module_ids):
self.fifo_readout.reset_rx()
self.fifo_readout.reset_fifo(self._selected_fifos)
self.fifo_readout.print_fei4_rx_status()
with self.access_files():
# some scans use this event to stop scan loop, clear event here to make another scan possible
self.stop_run.clear()
for t in self._scan_threads:
t.start()
while any([t.is_alive() for t in self._scan_threads]):
# if self.abort_run.is_set():
# break
for t in self._scan_threads:
try:
t.join(0.01)
except Exception:
self._scan_threads.remove(t)
self.handle_err(sys.exc_info())
# alive_threads = [t.name for t in self._scan_threads if (not t.join(10.0) and t.is_alive())]
# if alive_threads:
# raise RuntimeError("Scan thread(s) not finished: %s" % ", ".join(alive_threads))
self._scan_threads = []
for module_id in tx_module_ids:
if self.abort_run.is_set():
break
with self.access_module(module_id=module_id):
# set modules to conf mode by after finishing scan()
self.register_utils.set_conf_mode()
else:
for module_id in self._modules:
if self.abort_run.is_set():
break
# some scans use this event to stop scan loop, clear event here to make another scan possible
self.stop_run.clear()
with self.access_module(module_id=module_id):
logging.info('Scan parameter(s) for module %s: %s', module_id, ', '.join(['%s=%s' % (key, value) for (key, value) in self.scan_parameters._asdict().items()]) if self.scan_parameters else 'None')
with self.register.restored(name=self.run_number):
self.configure()
# set modules to run mode by before entering scan()
self.register_utils.set_run_mode()
self.fifo_readout.reset_rx()
self.fifo_readout.reset_fifo(self._selected_fifos)
self.fifo_readout.print_fei4_rx_status()
# some scans use this event to stop scan loop, clear event here to make another scan possible
self.stop_run.clear()
with self.access_files():
self.scan()
# set modules to conf mode by after finishing scan()
self.register_utils.set_conf_mode()
if self._modules:
self.fifo_readout.print_readout_status()
|
python
|
def do_run(self):
''' Start runs on all modules sequentially.
Sets properties to access current module properties.
'''
if self.broadcast_commands: # Broadcast FE commands
if self.threaded_scan:
with ExitStack() as restore_config_stack:
# Configure each FE individually
# Sort module config keys, configure broadcast modules first
for module_id in itertools.chain(self._tx_module_groups, self._modules):
if self.abort_run.is_set():
break
with self.access_module(module_id=module_id):
if module_id in self._modules:
module_id_str = "module " + module_id
else:
module_id_str = module_id.split('=', 1)
module_id_str[0] = module_id_str[0].replace("_", " ")
module_id_str = "=".join(module_id_str)
logging.info('Scan parameter(s) for %s: %s', module_id_str, ', '.join(['%s=%s' % (key, value) for (key, value) in self.scan_parameters._asdict().items()]) if self.scan_parameters else 'None')
# storing register values until scan has finished and then restore configuration
restore_config_stack.enter_context(self.register.restored(name=self.run_number))
self.configure()
for module_id in self._tx_module_groups:
if self.abort_run.is_set():
break
with self.access_module(module_id=module_id):
# set all modules to run mode by before entering scan()
self.register_utils.set_run_mode()
with self.access_module(module_id=None):
self.fifo_readout.reset_rx()
self.fifo_readout.reset_fifo(self._selected_fifos)
self.fifo_readout.print_fei4_rx_status()
with self.access_files():
self._scan_threads = []
for module_id in self._tx_module_groups:
if self.abort_run.is_set():
break
t = ExcThread(target=self.scan, name=module_id)
t.daemon = True # exiting program even when thread is alive
self._scan_threads.append(t)
for t in self._scan_threads:
t.start()
while any([t.is_alive() for t in self._scan_threads]):
# if self.abort_run.is_set():
# break
for t in self._scan_threads:
try:
t.join(0.01)
except Exception:
self._scan_threads.remove(t)
self.handle_err(sys.exc_info())
# alive_threads = [t.name for t in self._scan_threads if (not t.join(10.0) and t.is_alive())]
# if alive_threads:
# raise RuntimeError("Scan thread(s) not finished: %s" % ", ".join(alive_threads))
self._scan_threads = []
for module_id in self._tx_module_groups:
if self.abort_run.is_set():
break
with self.access_module(module_id=module_id):
# set modules to conf mode by after finishing scan()
self.register_utils.set_conf_mode()
else:
for tx_module_id, tx_group in self._tx_module_groups.items():
if self.abort_run.is_set():
break
with ExitStack() as restore_config_stack:
for module_id in itertools.chain([tx_module_id], tx_group):
if self.abort_run.is_set():
break
with self.access_module(module_id=module_id):
logging.info('Scan parameter(s) for module %s: %s', module_id, ', '.join(['%s=%s' % (key, value) for (key, value) in self.scan_parameters._asdict().items()]) if self.scan_parameters else 'None')
# storing register values until scan has finished and then restore configuration
restore_config_stack.enter_context(self.register.restored(name=self.run_number))
self.configure()
with self.access_module(module_id=tx_module_id):
# set all modules to run mode by before entering scan()
self.register_utils.set_run_mode()
self.fifo_readout.reset_rx()
self.fifo_readout.reset_fifo(self._selected_fifos)
self.fifo_readout.print_fei4_rx_status()
# some scans use this event to stop scan loop, clear event here to make another scan possible
self.stop_run.clear()
with self.access_files():
self.scan()
with self.access_module(module_id=tx_module_id):
# set modules to conf mode by after finishing scan()
self.register_utils.set_conf_mode()
else: # Scan each FE individually
if self.threaded_scan:
self._scan_threads = []
# loop over grpups of modules with different TX
for tx_module_ids in zip_nofill(*self._tx_module_groups.values()):
if self.abort_run.is_set():
break
with ExitStack() as restore_config_stack:
for module_id in tx_module_ids:
if self.abort_run.is_set():
break
with self.access_module(module_id=module_id):
logging.info('Scan parameter(s) for module %s: %s', module_id, ', '.join(['%s=%s' % (key, value) for (key, value) in self.scan_parameters._asdict().items()]) if self.scan_parameters else 'None')
# storing register values until scan has finished and then restore configuration
restore_config_stack.enter_context(self.register.restored(name=self.run_number))
self.configure()
# set modules to run mode by before entering scan()
self.register_utils.set_run_mode()
t = ExcThread(target=self.scan, name=module_id)
t.daemon = True # exiting program even when thread is alive
self._scan_threads.append(t)
with self.access_module(module_id=tx_module_ids):
self.fifo_readout.reset_rx()
self.fifo_readout.reset_fifo(self._selected_fifos)
self.fifo_readout.print_fei4_rx_status()
with self.access_files():
# some scans use this event to stop scan loop, clear event here to make another scan possible
self.stop_run.clear()
for t in self._scan_threads:
t.start()
while any([t.is_alive() for t in self._scan_threads]):
# if self.abort_run.is_set():
# break
for t in self._scan_threads:
try:
t.join(0.01)
except Exception:
self._scan_threads.remove(t)
self.handle_err(sys.exc_info())
# alive_threads = [t.name for t in self._scan_threads if (not t.join(10.0) and t.is_alive())]
# if alive_threads:
# raise RuntimeError("Scan thread(s) not finished: %s" % ", ".join(alive_threads))
self._scan_threads = []
for module_id in tx_module_ids:
if self.abort_run.is_set():
break
with self.access_module(module_id=module_id):
# set modules to conf mode by after finishing scan()
self.register_utils.set_conf_mode()
else:
for module_id in self._modules:
if self.abort_run.is_set():
break
# some scans use this event to stop scan loop, clear event here to make another scan possible
self.stop_run.clear()
with self.access_module(module_id=module_id):
logging.info('Scan parameter(s) for module %s: %s', module_id, ', '.join(['%s=%s' % (key, value) for (key, value) in self.scan_parameters._asdict().items()]) if self.scan_parameters else 'None')
with self.register.restored(name=self.run_number):
self.configure()
# set modules to run mode by before entering scan()
self.register_utils.set_run_mode()
self.fifo_readout.reset_rx()
self.fifo_readout.reset_fifo(self._selected_fifos)
self.fifo_readout.print_fei4_rx_status()
# some scans use this event to stop scan loop, clear event here to make another scan possible
self.stop_run.clear()
with self.access_files():
self.scan()
# set modules to conf mode by after finishing scan()
self.register_utils.set_conf_mode()
if self._modules:
self.fifo_readout.print_readout_status()
|
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Start runs on all modules sequentially.
Sets properties to access current module properties.
|
[
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4_run_base.py#L660-L830
|
SiLab-Bonn/pyBAR
|
pybar/fei4_run_base.py
|
Fei4RunBase.close
|
def close(self):
'''Releasing hardware resources.
'''
try:
self.dut.close()
except Exception:
logging.warning('Closing DUT was not successful')
else:
logging.debug('Closed DUT')
|
python
|
def close(self):
'''Releasing hardware resources.
'''
try:
self.dut.close()
except Exception:
logging.warning('Closing DUT was not successful')
else:
logging.debug('Closed DUT')
|
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] |
Releasing hardware resources.
|
[
"Releasing",
"hardware",
"resources",
"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4_run_base.py#L858-L866
|
SiLab-Bonn/pyBAR
|
pybar/fei4_run_base.py
|
Fei4RunBase.handle_data
|
def handle_data(self, data, new_file=False, flush=True):
'''Handling of the data.
Parameters
----------
data : list, tuple
Data tuple of the format (data (np.array), last_time (float), curr_time (float), status (int))
'''
for i, module_id in enumerate(self._selected_modules):
if data[i] is None:
continue
self._raw_data_files[module_id].append(data_iterable=data[i], scan_parameters=self._scan_parameters[module_id]._asdict(), new_file=new_file, flush=flush)
|
python
|
def handle_data(self, data, new_file=False, flush=True):
'''Handling of the data.
Parameters
----------
data : list, tuple
Data tuple of the format (data (np.array), last_time (float), curr_time (float), status (int))
'''
for i, module_id in enumerate(self._selected_modules):
if data[i] is None:
continue
self._raw_data_files[module_id].append(data_iterable=data[i], scan_parameters=self._scan_parameters[module_id]._asdict(), new_file=new_file, flush=flush)
|
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Handling of the data.
Parameters
----------
data : list, tuple
Data tuple of the format (data (np.array), last_time (float), curr_time (float), status (int))
|
[
"Handling",
"of",
"the",
"data",
"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4_run_base.py#L868-L879
|
SiLab-Bonn/pyBAR
|
pybar/fei4_run_base.py
|
Fei4RunBase.handle_err
|
def handle_err(self, exc):
'''Handling of Exceptions.
Parameters
----------
exc : list, tuple
Information of the exception of the format (type, value, traceback).
Uses the return value of sys.exc_info().
'''
if self.reset_rx_on_error and isinstance(exc[1], (RxSyncError, EightbTenbError)):
self.fifo_readout.print_readout_status()
self.fifo_readout.reset_rx()
else:
# print just the first error massage
if not self.abort_run.is_set():
self.abort(msg=exc[1].__class__.__name__ + ": " + str(exc[1]))
self.err_queue.put(exc)
|
python
|
def handle_err(self, exc):
'''Handling of Exceptions.
Parameters
----------
exc : list, tuple
Information of the exception of the format (type, value, traceback).
Uses the return value of sys.exc_info().
'''
if self.reset_rx_on_error and isinstance(exc[1], (RxSyncError, EightbTenbError)):
self.fifo_readout.print_readout_status()
self.fifo_readout.reset_rx()
else:
# print just the first error massage
if not self.abort_run.is_set():
self.abort(msg=exc[1].__class__.__name__ + ": " + str(exc[1]))
self.err_queue.put(exc)
|
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Handling of Exceptions.
Parameters
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exc : list, tuple
Information of the exception of the format (type, value, traceback).
Uses the return value of sys.exc_info().
|
[
"Handling",
"of",
"Exceptions",
"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4_run_base.py#L881-L897
|
SiLab-Bonn/pyBAR
|
pybar/fei4_run_base.py
|
Fei4RunBase.get_configuration
|
def get_configuration(self, module_id, run_number=None):
''' Returns the configuration for a given module ID.
The working directory is searched for a file matching the module_id with the
given run number. If no run number is defined the last successfull run defines
the run number.
'''
def find_file(run_number):
module_path = self.get_module_path(module_id)
for root, _, files in os.walk(module_path):
for cfgfile in files:
cfg_root, cfg_ext = os.path.splitext(cfgfile)
if cfg_root.startswith(''.join([str(run_number), '_', module_id])) and cfg_ext.endswith(".cfg"):
return os.path.join(root, cfgfile)
if not run_number:
run_numbers = sorted(self._get_run_numbers(status='FINISHED').keys(), reverse=True)
found_fin_run_cfg = True
if not run_numbers:
return None
last_fin_run = run_numbers[0]
for run_number in run_numbers:
cfg_file = find_file(run_number)
if cfg_file:
if not found_fin_run_cfg:
logging.warning("Module '%s' has no configuration for run %d, use config of run %d", module_id, last_fin_run, run_number)
return cfg_file
else:
found_fin_run_cfg = False
else:
cfg_file = find_file(run_number)
if cfg_file:
return cfg_file
else:
raise ValueError('Found no configuration with run number %s' % run_number)
|
python
|
def get_configuration(self, module_id, run_number=None):
''' Returns the configuration for a given module ID.
The working directory is searched for a file matching the module_id with the
given run number. If no run number is defined the last successfull run defines
the run number.
'''
def find_file(run_number):
module_path = self.get_module_path(module_id)
for root, _, files in os.walk(module_path):
for cfgfile in files:
cfg_root, cfg_ext = os.path.splitext(cfgfile)
if cfg_root.startswith(''.join([str(run_number), '_', module_id])) and cfg_ext.endswith(".cfg"):
return os.path.join(root, cfgfile)
if not run_number:
run_numbers = sorted(self._get_run_numbers(status='FINISHED').keys(), reverse=True)
found_fin_run_cfg = True
if not run_numbers:
return None
last_fin_run = run_numbers[0]
for run_number in run_numbers:
cfg_file = find_file(run_number)
if cfg_file:
if not found_fin_run_cfg:
logging.warning("Module '%s' has no configuration for run %d, use config of run %d", module_id, last_fin_run, run_number)
return cfg_file
else:
found_fin_run_cfg = False
else:
cfg_file = find_file(run_number)
if cfg_file:
return cfg_file
else:
raise ValueError('Found no configuration with run number %s' % run_number)
|
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Returns the configuration for a given module ID.
The working directory is searched for a file matching the module_id with the
given run number. If no run number is defined the last successfull run defines
the run number.
|
[
"Returns",
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"ID",
"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4_run_base.py#L904-L938
|
SiLab-Bonn/pyBAR
|
pybar/fei4_run_base.py
|
Fei4RunBase.select_module
|
def select_module(self, module_id):
''' Select module and give access to the module.
'''
if not isinstance(module_id, basestring) and isinstance(module_id, Iterable) and set(module_id) - set(self._modules):
raise ValueError('Module IDs invalid:' % ", ".join(set(module_id) - set(self._modules)))
if isinstance(module_id, basestring) and module_id not in self._module_cfgs:
raise ValueError('Module ID "%s" is not valid' % module_id)
if self._current_module_handle is not None:
raise RuntimeError('Module handle "%s" cannot be set because another module is active' % module_id)
if module_id is None:
self._selected_modules = self._modules.keys()
elif not isinstance(module_id, basestring) and isinstance(module_id, Iterable):
self._selected_modules = module_id
elif module_id in self._modules:
self._selected_modules = [module_id]
elif module_id in self._tx_module_groups:
self._selected_modules = self._tx_module_groups[module_id]
else:
RuntimeError('Cannot open files. Module handle "%s" is not valid.' % self.current_module_handle)
# FIFO readout
self._selected_fifos = list(set([module_cfg['FIFO'] for (name, module_cfg) in self._module_cfgs.items() if name in self._selected_modules]))
# Module filter functions dict for quick lookup
self._readout_fifos = []
self._filter = []
self._converter = []
for selected_module_id in self._selected_modules:
module_cfg = self._module_cfgs[selected_module_id]
self._readout_fifos.append(module_cfg['FIFO'])
if 'tdc_channel' not in module_cfg:
tdc_filter = false
self._converter.append(None)
elif module_cfg['tdc_channel'] is None:
tdc_filter = is_tdc_word
self._converter.append(convert_tdc_to_channel(channel=module_cfg['tdc_channel'])) # for the raw data analyzer
else:
tdc_filter = logical_and(is_tdc_word, is_tdc_from_channel(module_cfg['tdc_channel']))
self._converter.append(convert_tdc_to_channel(channel=module_cfg['tdc_channel'])) # for the raw data analyzer
if 'rx_channel' not in module_cfg:
self._filter.append(logical_or(is_trigger_word, tdc_filter))
elif module_cfg['rx_channel'] is None:
self._filter.append(logical_or(is_trigger_word, logical_or(tdc_filter, is_fe_word)))
else:
self._filter.append(logical_or(is_trigger_word, logical_or(tdc_filter, logical_and(is_fe_word, is_data_from_channel(module_cfg['rx_channel'])))))
# select readout channels and report sync status only from actively selected modules
self._enabled_fe_channels = list(set([config['RX'] for (name, config) in self._module_cfgs.items() if name in self._selected_modules]))
# enabling specific TX channels
tx_channels = list(set([1 << config['tx_channel'] for (name, config) in self._module_cfgs.items() if name in self._selected_modules]))
if tx_channels:
self.dut['TX']['OUTPUT_ENABLE'] = reduce(lambda x, y: x | y, tx_channels)
else:
self.dut['TX']['OUTPUT_ENABLE'] = 0
if not isinstance(module_id, basestring) and isinstance(module_id, Iterable):
self._current_module_handle = None
else:
self._current_module_handle = module_id
if module_id is not None and isinstance(module_id, basestring):
current_thread().name = module_id
|
python
|
def select_module(self, module_id):
''' Select module and give access to the module.
'''
if not isinstance(module_id, basestring) and isinstance(module_id, Iterable) and set(module_id) - set(self._modules):
raise ValueError('Module IDs invalid:' % ", ".join(set(module_id) - set(self._modules)))
if isinstance(module_id, basestring) and module_id not in self._module_cfgs:
raise ValueError('Module ID "%s" is not valid' % module_id)
if self._current_module_handle is not None:
raise RuntimeError('Module handle "%s" cannot be set because another module is active' % module_id)
if module_id is None:
self._selected_modules = self._modules.keys()
elif not isinstance(module_id, basestring) and isinstance(module_id, Iterable):
self._selected_modules = module_id
elif module_id in self._modules:
self._selected_modules = [module_id]
elif module_id in self._tx_module_groups:
self._selected_modules = self._tx_module_groups[module_id]
else:
RuntimeError('Cannot open files. Module handle "%s" is not valid.' % self.current_module_handle)
# FIFO readout
self._selected_fifos = list(set([module_cfg['FIFO'] for (name, module_cfg) in self._module_cfgs.items() if name in self._selected_modules]))
# Module filter functions dict for quick lookup
self._readout_fifos = []
self._filter = []
self._converter = []
for selected_module_id in self._selected_modules:
module_cfg = self._module_cfgs[selected_module_id]
self._readout_fifos.append(module_cfg['FIFO'])
if 'tdc_channel' not in module_cfg:
tdc_filter = false
self._converter.append(None)
elif module_cfg['tdc_channel'] is None:
tdc_filter = is_tdc_word
self._converter.append(convert_tdc_to_channel(channel=module_cfg['tdc_channel'])) # for the raw data analyzer
else:
tdc_filter = logical_and(is_tdc_word, is_tdc_from_channel(module_cfg['tdc_channel']))
self._converter.append(convert_tdc_to_channel(channel=module_cfg['tdc_channel'])) # for the raw data analyzer
if 'rx_channel' not in module_cfg:
self._filter.append(logical_or(is_trigger_word, tdc_filter))
elif module_cfg['rx_channel'] is None:
self._filter.append(logical_or(is_trigger_word, logical_or(tdc_filter, is_fe_word)))
else:
self._filter.append(logical_or(is_trigger_word, logical_or(tdc_filter, logical_and(is_fe_word, is_data_from_channel(module_cfg['rx_channel'])))))
# select readout channels and report sync status only from actively selected modules
self._enabled_fe_channels = list(set([config['RX'] for (name, config) in self._module_cfgs.items() if name in self._selected_modules]))
# enabling specific TX channels
tx_channels = list(set([1 << config['tx_channel'] for (name, config) in self._module_cfgs.items() if name in self._selected_modules]))
if tx_channels:
self.dut['TX']['OUTPUT_ENABLE'] = reduce(lambda x, y: x | y, tx_channels)
else:
self.dut['TX']['OUTPUT_ENABLE'] = 0
if not isinstance(module_id, basestring) and isinstance(module_id, Iterable):
self._current_module_handle = None
else:
self._current_module_handle = module_id
if module_id is not None and isinstance(module_id, basestring):
current_thread().name = module_id
|
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Select module and give access to the module.
|
[
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4_run_base.py#L1014-L1077
|
SiLab-Bonn/pyBAR
|
pybar/fei4_run_base.py
|
Fei4RunBase.deselect_module
|
def deselect_module(self):
''' Deselect module and cleanup.
'''
self._enabled_fe_channels = [] # ignore any RX sync errors
self._readout_fifos = []
self._filter = []
self._converter = []
self.dut['TX']['OUTPUT_ENABLE'] = 0
self._current_module_handle = None
if isinstance(current_thread(), _MainThread):
current_thread().name = "MainThread"
|
python
|
def deselect_module(self):
''' Deselect module and cleanup.
'''
self._enabled_fe_channels = [] # ignore any RX sync errors
self._readout_fifos = []
self._filter = []
self._converter = []
self.dut['TX']['OUTPUT_ENABLE'] = 0
self._current_module_handle = None
if isinstance(current_thread(), _MainThread):
current_thread().name = "MainThread"
|
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Deselect module and cleanup.
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[
"Deselect",
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"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4_run_base.py#L1079-L1089
|
SiLab-Bonn/pyBAR
|
pybar/fei4_run_base.py
|
Fei4RunBase.enter_sync
|
def enter_sync(self):
''' Waiting for all threads to appear, then continue.
'''
if self._scan_threads and self.current_module_handle not in [t.name for t in self._scan_threads]:
raise RuntimeError('Thread name "%s" is not valid.')
if self._scan_threads and self.current_module_handle in self._curr_sync_threads:
raise RuntimeError('Thread "%s" is already actively reading FIFO.')
with self._sync_lock:
self._curr_sync_threads.append(self.current_module_handle)
self._enter_sync_event.clear()
while not self._enter_sync_event.wait(0.01):
if self.abort_run.is_set():
break
with self._sync_lock:
if len(set(self._curr_sync_threads) & set([t.name for t in self._scan_threads if t.is_alive()])) == len(set([t.name for t in self._scan_threads if t.is_alive()])) or not self._scan_threads:
self._enter_sync_event.set()
|
python
|
def enter_sync(self):
''' Waiting for all threads to appear, then continue.
'''
if self._scan_threads and self.current_module_handle not in [t.name for t in self._scan_threads]:
raise RuntimeError('Thread name "%s" is not valid.')
if self._scan_threads and self.current_module_handle in self._curr_sync_threads:
raise RuntimeError('Thread "%s" is already actively reading FIFO.')
with self._sync_lock:
self._curr_sync_threads.append(self.current_module_handle)
self._enter_sync_event.clear()
while not self._enter_sync_event.wait(0.01):
if self.abort_run.is_set():
break
with self._sync_lock:
if len(set(self._curr_sync_threads) & set([t.name for t in self._scan_threads if t.is_alive()])) == len(set([t.name for t in self._scan_threads if t.is_alive()])) or not self._scan_threads:
self._enter_sync_event.set()
|
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[
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4_run_base.py#L1169-L1184
|
SiLab-Bonn/pyBAR
|
pybar/fei4_run_base.py
|
Fei4RunBase.exit_sync
|
def exit_sync(self):
''' Waiting for all threads to appear, then continue.
'''
if self._scan_threads and self.current_module_handle not in [t.name for t in self._scan_threads]:
raise RuntimeError('Thread name "%s" is not valid.')
if self._scan_threads and self.current_module_handle not in self._curr_sync_threads:
raise RuntimeError('Thread "%s" is not reading FIFO.')
with self._sync_lock:
self._curr_sync_threads.remove(self.current_module_handle)
self._exit_sync_event.clear()
while not self._exit_sync_event.wait(0.01):
if self.abort_run.is_set():
break
with self._sync_lock:
if len(set(self._curr_sync_threads) & set([t.name for t in self._scan_threads if t.is_alive()])) == 0 or not self._scan_threads:
self._exit_sync_event.set()
|
python
|
def exit_sync(self):
''' Waiting for all threads to appear, then continue.
'''
if self._scan_threads and self.current_module_handle not in [t.name for t in self._scan_threads]:
raise RuntimeError('Thread name "%s" is not valid.')
if self._scan_threads and self.current_module_handle not in self._curr_sync_threads:
raise RuntimeError('Thread "%s" is not reading FIFO.')
with self._sync_lock:
self._curr_sync_threads.remove(self.current_module_handle)
self._exit_sync_event.clear()
while not self._exit_sync_event.wait(0.01):
if self.abort_run.is_set():
break
with self._sync_lock:
if len(set(self._curr_sync_threads) & set([t.name for t in self._scan_threads if t.is_alive()])) == 0 or not self._scan_threads:
self._exit_sync_event.set()
|
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[
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] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4_run_base.py#L1186-L1201
|
SiLab-Bonn/pyBAR
|
pybar/fei4_run_base.py
|
Fei4RunBase.readout
|
def readout(self, *args, **kwargs):
''' Running the FIFO readout while executing other statements.
Starting and stopping of the FIFO readout is synchronized between the threads.
'''
timeout = kwargs.pop('timeout', 10.0)
self.start_readout(*args, **kwargs)
try:
yield
finally:
try:
self.stop_readout(timeout=timeout)
except Exception:
# in case something fails, call this on last resort
# if run was aborted, immediately stop readout
if self.abort_run.is_set():
with self._readout_lock:
if self.fifo_readout.is_running:
self.fifo_readout.stop(timeout=0.0)
|
python
|
def readout(self, *args, **kwargs):
''' Running the FIFO readout while executing other statements.
Starting and stopping of the FIFO readout is synchronized between the threads.
'''
timeout = kwargs.pop('timeout', 10.0)
self.start_readout(*args, **kwargs)
try:
yield
finally:
try:
self.stop_readout(timeout=timeout)
except Exception:
# in case something fails, call this on last resort
# if run was aborted, immediately stop readout
if self.abort_run.is_set():
with self._readout_lock:
if self.fifo_readout.is_running:
self.fifo_readout.stop(timeout=0.0)
|
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Running the FIFO readout while executing other statements.
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|
[
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"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4_run_base.py#L1204-L1222
|
SiLab-Bonn/pyBAR
|
pybar/fei4_run_base.py
|
Fei4RunBase.start_readout
|
def start_readout(self, *args, **kwargs):
''' Starting the FIFO readout.
Starting of the FIFO readout is executed only once by a random thread.
Starting of the FIFO readout is synchronized between all threads reading out the FIFO.
'''
# Pop parameters for fifo_readout.start
callback = kwargs.pop('callback', self.handle_data)
errback = kwargs.pop('errback', self.handle_err)
reset_rx = kwargs.pop('reset_rx', True)
reset_fifo = kwargs.pop('reset_fifo', True)
fill_buffer = kwargs.pop('fill_buffer', False)
no_data_timeout = kwargs.pop('no_data_timeout', None)
enabled_fe_channels = kwargs.pop('enabled_fe_channels', self._enabled_fe_channels)
if args or kwargs:
self.set_scan_parameters(*args, **kwargs)
if self._scan_threads and self.current_module_handle not in [t.name for t in self._scan_threads]:
raise RuntimeError('Thread name "%s" is not valid.' % t.name)
if self._scan_threads and self.current_module_handle in self._curr_readout_threads:
raise RuntimeError('Thread "%s" is already actively reading FIFO.')
with self._readout_lock:
self._curr_readout_threads.append(self.current_module_handle)
self._starting_readout_event.clear()
while not self._starting_readout_event.wait(0.01):
if self.abort_run.is_set():
break
with self._readout_lock:
if len(set(self._curr_readout_threads) & set([t.name for t in self._scan_threads if t.is_alive()])) == len(set([t.name for t in self._scan_threads if t.is_alive()])) or not self._scan_threads:
if not self.fifo_readout.is_running:
self.fifo_readout.start(fifos=self._selected_fifos, callback=callback, errback=errback, reset_rx=reset_rx, reset_fifo=reset_fifo, fill_buffer=fill_buffer, no_data_timeout=no_data_timeout, filter_func=self._filter, converter_func=self._converter, fifo_select=self._readout_fifos, enabled_fe_channels=enabled_fe_channels)
self._starting_readout_event.set()
|
python
|
def start_readout(self, *args, **kwargs):
''' Starting the FIFO readout.
Starting of the FIFO readout is executed only once by a random thread.
Starting of the FIFO readout is synchronized between all threads reading out the FIFO.
'''
# Pop parameters for fifo_readout.start
callback = kwargs.pop('callback', self.handle_data)
errback = kwargs.pop('errback', self.handle_err)
reset_rx = kwargs.pop('reset_rx', True)
reset_fifo = kwargs.pop('reset_fifo', True)
fill_buffer = kwargs.pop('fill_buffer', False)
no_data_timeout = kwargs.pop('no_data_timeout', None)
enabled_fe_channels = kwargs.pop('enabled_fe_channels', self._enabled_fe_channels)
if args or kwargs:
self.set_scan_parameters(*args, **kwargs)
if self._scan_threads and self.current_module_handle not in [t.name for t in self._scan_threads]:
raise RuntimeError('Thread name "%s" is not valid.' % t.name)
if self._scan_threads and self.current_module_handle in self._curr_readout_threads:
raise RuntimeError('Thread "%s" is already actively reading FIFO.')
with self._readout_lock:
self._curr_readout_threads.append(self.current_module_handle)
self._starting_readout_event.clear()
while not self._starting_readout_event.wait(0.01):
if self.abort_run.is_set():
break
with self._readout_lock:
if len(set(self._curr_readout_threads) & set([t.name for t in self._scan_threads if t.is_alive()])) == len(set([t.name for t in self._scan_threads if t.is_alive()])) or not self._scan_threads:
if not self.fifo_readout.is_running:
self.fifo_readout.start(fifos=self._selected_fifos, callback=callback, errback=errback, reset_rx=reset_rx, reset_fifo=reset_fifo, fill_buffer=fill_buffer, no_data_timeout=no_data_timeout, filter_func=self._filter, converter_func=self._converter, fifo_select=self._readout_fifos, enabled_fe_channels=enabled_fe_channels)
self._starting_readout_event.set()
|
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|
[
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4_run_base.py#L1224-L1254
|
SiLab-Bonn/pyBAR
|
pybar/fei4_run_base.py
|
Fei4RunBase.stop_readout
|
def stop_readout(self, timeout=10.0):
''' Stopping the FIFO readout.
Stopping of the FIFO readout is executed only once by a random thread.
Stopping of the FIFO readout is synchronized between all threads reading out the FIFO.
'''
if self._scan_threads and self.current_module_handle not in [t.name for t in self._scan_threads]:
raise RuntimeError('Thread name "%s" is not valid.')
if self._scan_threads and self.current_module_handle not in self._curr_readout_threads:
raise RuntimeError('Thread "%s" is not reading FIFO.')
with self._readout_lock:
self._curr_readout_threads.remove(self.current_module_handle)
self._stopping_readout_event.clear()
while not self._stopping_readout_event.wait(0.01):
with self._readout_lock:
if len(set(self._curr_readout_threads) & set([t.name for t in self._scan_threads if t.is_alive()])) == 0 or not self._scan_threads or self.abort_run.is_set():
if self.fifo_readout.is_running:
self.fifo_readout.stop(timeout=timeout)
self._stopping_readout_event.set()
|
python
|
def stop_readout(self, timeout=10.0):
''' Stopping the FIFO readout.
Stopping of the FIFO readout is executed only once by a random thread.
Stopping of the FIFO readout is synchronized between all threads reading out the FIFO.
'''
if self._scan_threads and self.current_module_handle not in [t.name for t in self._scan_threads]:
raise RuntimeError('Thread name "%s" is not valid.')
if self._scan_threads and self.current_module_handle not in self._curr_readout_threads:
raise RuntimeError('Thread "%s" is not reading FIFO.')
with self._readout_lock:
self._curr_readout_threads.remove(self.current_module_handle)
self._stopping_readout_event.clear()
while not self._stopping_readout_event.wait(0.01):
with self._readout_lock:
if len(set(self._curr_readout_threads) & set([t.name for t in self._scan_threads if t.is_alive()])) == 0 or not self._scan_threads or self.abort_run.is_set():
if self.fifo_readout.is_running:
self.fifo_readout.stop(timeout=timeout)
self._stopping_readout_event.set()
|
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Stopping the FIFO readout.
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Stopping of the FIFO readout is synchronized between all threads reading out the FIFO.
|
[
"Stopping",
"the",
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"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4_run_base.py#L1256-L1274
|
SiLab-Bonn/pyBAR
|
pybar/scans/analyze_timewalk.py
|
get_charge
|
def get_charge(max_tdc, tdc_calibration_values, tdc_pixel_calibration): # Return the charge from calibration
''' Interpolatet the TDC calibration for each pixel from 0 to max_tdc'''
charge_calibration = np.zeros(shape=(80, 336, max_tdc))
for column in range(80):
for row in range(336):
actual_pixel_calibration = tdc_pixel_calibration[column, row, :]
if np.any(actual_pixel_calibration != 0) and np.any(np.isfinite(actual_pixel_calibration)):
selected_measurements = np.isfinite(actual_pixel_calibration) # Select valid calibration steps
selected_actual_pixel_calibration = actual_pixel_calibration[selected_measurements]
selected_tdc_calibration_values = tdc_calibration_values[selected_measurements]
interpolation = interp1d(x=selected_actual_pixel_calibration, y=selected_tdc_calibration_values, kind='slinear', bounds_error=False, fill_value=0)
charge_calibration[column, row, :] = interpolation(np.arange(max_tdc))
return charge_calibration
|
python
|
def get_charge(max_tdc, tdc_calibration_values, tdc_pixel_calibration): # Return the charge from calibration
''' Interpolatet the TDC calibration for each pixel from 0 to max_tdc'''
charge_calibration = np.zeros(shape=(80, 336, max_tdc))
for column in range(80):
for row in range(336):
actual_pixel_calibration = tdc_pixel_calibration[column, row, :]
if np.any(actual_pixel_calibration != 0) and np.any(np.isfinite(actual_pixel_calibration)):
selected_measurements = np.isfinite(actual_pixel_calibration) # Select valid calibration steps
selected_actual_pixel_calibration = actual_pixel_calibration[selected_measurements]
selected_tdc_calibration_values = tdc_calibration_values[selected_measurements]
interpolation = interp1d(x=selected_actual_pixel_calibration, y=selected_tdc_calibration_values, kind='slinear', bounds_error=False, fill_value=0)
charge_calibration[column, row, :] = interpolation(np.arange(max_tdc))
return charge_calibration
|
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[
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/scans/analyze_timewalk.py#L21-L33
|
SiLab-Bonn/pyBAR
|
pybar/scans/analyze_timewalk.py
|
get_charge_calibration
|
def get_charge_calibration(calibation_file, max_tdc):
''' Open the hit or calibration file and return the calibration per pixel'''
with tb.open_file(calibation_file, mode="r") as in_file_calibration_h5:
tdc_calibration = in_file_calibration_h5.root.HitOrCalibration[:, :, :, 1]
tdc_calibration_values = in_file_calibration_h5.root.HitOrCalibration.attrs.scan_parameter_values[:]
return get_charge(max_tdc, tdc_calibration_values, tdc_calibration)
|
python
|
def get_charge_calibration(calibation_file, max_tdc):
''' Open the hit or calibration file and return the calibration per pixel'''
with tb.open_file(calibation_file, mode="r") as in_file_calibration_h5:
tdc_calibration = in_file_calibration_h5.root.HitOrCalibration[:, :, :, 1]
tdc_calibration_values = in_file_calibration_h5.root.HitOrCalibration.attrs.scan_parameter_values[:]
return get_charge(max_tdc, tdc_calibration_values, tdc_calibration)
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/scans/analyze_timewalk.py#L36-L41
|
SiLab-Bonn/pyBAR
|
pybar/ViTablesPlugin/pybar_plugin.py
|
pyBarPlugin.addEntry
|
def addEntry(self):
"""Add the `Plot pyBAR data`. entry to `Dataset` menu.
"""
export_icon = QtGui.QIcon()
pixmap = QtGui.QPixmap(os.path.join(PLUGINSDIR,
'csv/icons/document-export.png'))
export_icon.addPixmap(pixmap, QtGui.QIcon.Normal, QtGui.QIcon.On)
self.plot_action = QtGui.QAction(
translate('PlotpyBARdata',
"Plot data with pyBAR plugin",
"Plot data with pyBAR plugin"),
self,
shortcut=QtGui.QKeySequence.UnknownKey, triggered=self.plot,
icon=export_icon,
statusTip=translate('PlotpyBARdata',
"Plotting of selected data with pyBAR",
"Status bar text for the Dataset -> Plot pyBAR data... action"))
# Add the action to the Dataset menu
menu = self.vtgui.dataset_menu
menu.addSeparator()
menu.addAction(self.plot_action)
# Add the action to the leaf context menu
cmenu = self.vtgui.leaf_node_cm
cmenu.addSeparator()
cmenu.addAction(self.plot_action)
|
python
|
def addEntry(self):
"""Add the `Plot pyBAR data`. entry to `Dataset` menu.
"""
export_icon = QtGui.QIcon()
pixmap = QtGui.QPixmap(os.path.join(PLUGINSDIR,
'csv/icons/document-export.png'))
export_icon.addPixmap(pixmap, QtGui.QIcon.Normal, QtGui.QIcon.On)
self.plot_action = QtGui.QAction(
translate('PlotpyBARdata',
"Plot data with pyBAR plugin",
"Plot data with pyBAR plugin"),
self,
shortcut=QtGui.QKeySequence.UnknownKey, triggered=self.plot,
icon=export_icon,
statusTip=translate('PlotpyBARdata',
"Plotting of selected data with pyBAR",
"Status bar text for the Dataset -> Plot pyBAR data... action"))
# Add the action to the Dataset menu
menu = self.vtgui.dataset_menu
menu.addSeparator()
menu.addAction(self.plot_action)
# Add the action to the leaf context menu
cmenu = self.vtgui.leaf_node_cm
cmenu.addSeparator()
cmenu.addAction(self.plot_action)
|
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Add the `Plot pyBAR data`. entry to `Dataset` menu.
|
[
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"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/ViTablesPlugin/pybar_plugin.py#L145-L172
|
SiLab-Bonn/pyBAR
|
pybar/ViTablesPlugin/pybar_plugin.py
|
pyBarPlugin.updateDatasetMenu
|
def updateDatasetMenu(self):
"""Update the `export` QAction when the Dataset menu is pulled down.
This method is a slot. See class ctor for details.
"""
enabled = True
current = self.vtgui.dbs_tree_view.currentIndex()
if current:
leaf = self.vtgui.dbs_tree_model.nodeFromIndex(current)
if leaf.node_kind in (u'group', u'root group'):
enabled = False
self.plot_action.setEnabled(enabled)
|
python
|
def updateDatasetMenu(self):
"""Update the `export` QAction when the Dataset menu is pulled down.
This method is a slot. See class ctor for details.
"""
enabled = True
current = self.vtgui.dbs_tree_view.currentIndex()
if current:
leaf = self.vtgui.dbs_tree_model.nodeFromIndex(current)
if leaf.node_kind in (u'group', u'root group'):
enabled = False
self.plot_action.setEnabled(enabled)
|
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Update the `export` QAction when the Dataset menu is pulled down.
This method is a slot. See class ctor for details.
|
[
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"QAction",
"when",
"the",
"Dataset",
"menu",
"is",
"pulled",
"down",
"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/ViTablesPlugin/pybar_plugin.py#L174-L186
|
SiLab-Bonn/pyBAR
|
pybar/ViTablesPlugin/pybar_plugin.py
|
pyBarPlugin.plot
|
def plot(self):
"""Export a given dataset to a `CSV` file.
This method is a slot connected to the `export` QAction. See the
:meth:`addEntry` method for details.
"""
# The PyTables node tied to the current leaf of the databases tree
current = self.vtgui.dbs_tree_view.currentIndex()
leaf = self.vtgui.dbs_tree_model.nodeFromIndex(current).node
data_name = leaf.name
hists_1d = ['HistRelBcid', 'HistErrorCounter', 'HistTriggerErrorCounter', 'HistServiceRecord', 'HistTot', 'HistTdc', 'HistClusterTot', 'HistClusterSize']
hists_2d = ['HistOcc', 'Enable', 'Imon', 'C_High', 'EnableDigInj', 'C_Low', 'FDAC', 'TDAC', 'HistTdcPixel', 'HistTotPixel', 'HistThreshold', 'HistNoise', 'HistThresholdFitted', 'HistNoiseFitted', 'HistThresholdFittedCalib', 'HistNoiseFittedCalib']
if data_name in hists_1d:
plot_1d_hist(hist=leaf[:], title=data_name)
elif data_name in hists_2d:
if data_name == 'HistOcc':
leaf = np.sum(leaf[:], axis=2)
plot_2d_hist(hist=leaf[:], title=data_name)
elif 'Table' in str(type(leaf)) and len(leaf[:].dtype.names) <= 3: # detect tables with less than 4 columns
plot_table(leaf[:], title=data_name)
elif data_name == 'HitOrCalibration':
print 'Comming soon'
else:
print 'Plotting', data_name, '(%s) is not supported!' % type(leaf)
|
python
|
def plot(self):
"""Export a given dataset to a `CSV` file.
This method is a slot connected to the `export` QAction. See the
:meth:`addEntry` method for details.
"""
# The PyTables node tied to the current leaf of the databases tree
current = self.vtgui.dbs_tree_view.currentIndex()
leaf = self.vtgui.dbs_tree_model.nodeFromIndex(current).node
data_name = leaf.name
hists_1d = ['HistRelBcid', 'HistErrorCounter', 'HistTriggerErrorCounter', 'HistServiceRecord', 'HistTot', 'HistTdc', 'HistClusterTot', 'HistClusterSize']
hists_2d = ['HistOcc', 'Enable', 'Imon', 'C_High', 'EnableDigInj', 'C_Low', 'FDAC', 'TDAC', 'HistTdcPixel', 'HistTotPixel', 'HistThreshold', 'HistNoise', 'HistThresholdFitted', 'HistNoiseFitted', 'HistThresholdFittedCalib', 'HistNoiseFittedCalib']
if data_name in hists_1d:
plot_1d_hist(hist=leaf[:], title=data_name)
elif data_name in hists_2d:
if data_name == 'HistOcc':
leaf = np.sum(leaf[:], axis=2)
plot_2d_hist(hist=leaf[:], title=data_name)
elif 'Table' in str(type(leaf)) and len(leaf[:].dtype.names) <= 3: # detect tables with less than 4 columns
plot_table(leaf[:], title=data_name)
elif data_name == 'HitOrCalibration':
print 'Comming soon'
else:
print 'Plotting', data_name, '(%s) is not supported!' % type(leaf)
|
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|
[
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] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/ViTablesPlugin/pybar_plugin.py#L188-L214
|
SiLab-Bonn/pyBAR
|
pybar/ViTablesPlugin/pybar_plugin.py
|
pyBarPlugin.helpAbout
|
def helpAbout(self):
"""Brief description of the plugin.
"""
# Text to be displayed
about_text = translate('pyBarPlugin',
"""<qt>
<p>Data plotting plug-in for pyBAR.
</qt>""",
'About')
descr = dict(module_name='pyBarPlugin',
folder=PLUGINSDIR,
version=__version__,
plugin_name='pyBarPlugin',
author='David-Leon Pohl <david-leon.pohl@rub.de>, Jens Janssen <janssen@physik.uni-bonn.de>',
descr=about_text)
return descr
|
python
|
def helpAbout(self):
"""Brief description of the plugin.
"""
# Text to be displayed
about_text = translate('pyBarPlugin',
"""<qt>
<p>Data plotting plug-in for pyBAR.
</qt>""",
'About')
descr = dict(module_name='pyBarPlugin',
folder=PLUGINSDIR,
version=__version__,
plugin_name='pyBarPlugin',
author='David-Leon Pohl <david-leon.pohl@rub.de>, Jens Janssen <janssen@physik.uni-bonn.de>',
descr=about_text)
return descr
|
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Brief description of the plugin.
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] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/ViTablesPlugin/pybar_plugin.py#L216-L234
|
SiLab-Bonn/pyBAR
|
pybar/daq/fei4_raw_data.py
|
send_meta_data
|
def send_meta_data(socket, conf, name):
'''Sends the config via ZeroMQ to a specified socket. Is called at the beginning of a run and when the config changes. Conf can be any config dictionary.
'''
meta_data = dict(
name=name,
conf=conf
)
try:
socket.send_json(meta_data, flags=zmq.NOBLOCK)
except zmq.Again:
pass
|
python
|
def send_meta_data(socket, conf, name):
'''Sends the config via ZeroMQ to a specified socket. Is called at the beginning of a run and when the config changes. Conf can be any config dictionary.
'''
meta_data = dict(
name=name,
conf=conf
)
try:
socket.send_json(meta_data, flags=zmq.NOBLOCK)
except zmq.Again:
pass
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/daq/fei4_raw_data.py#L13-L23
|
SiLab-Bonn/pyBAR
|
pybar/daq/fei4_raw_data.py
|
send_data
|
def send_data(socket, data, scan_parameters={}, name='ReadoutData'):
'''Sends the data of every read out (raw data and meta data) via ZeroMQ to a specified socket
'''
if not scan_parameters:
scan_parameters = {}
data_meta_data = dict(
name=name,
dtype=str(data[0].dtype),
shape=data[0].shape,
timestamp_start=data[1], # float
timestamp_stop=data[2], # float
readout_error=data[3], # int
scan_parameters=scan_parameters # dict
)
try:
socket.send_json(data_meta_data, flags=zmq.SNDMORE | zmq.NOBLOCK)
socket.send(data[0], flags=zmq.NOBLOCK) # PyZMQ supports sending numpy arrays without copying any data
except zmq.Again:
pass
|
python
|
def send_data(socket, data, scan_parameters={}, name='ReadoutData'):
'''Sends the data of every read out (raw data and meta data) via ZeroMQ to a specified socket
'''
if not scan_parameters:
scan_parameters = {}
data_meta_data = dict(
name=name,
dtype=str(data[0].dtype),
shape=data[0].shape,
timestamp_start=data[1], # float
timestamp_stop=data[2], # float
readout_error=data[3], # int
scan_parameters=scan_parameters # dict
)
try:
socket.send_json(data_meta_data, flags=zmq.SNDMORE | zmq.NOBLOCK)
socket.send(data[0], flags=zmq.NOBLOCK) # PyZMQ supports sending numpy arrays without copying any data
except zmq.Again:
pass
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/daq/fei4_raw_data.py#L26-L44
|
SiLab-Bonn/pyBAR
|
pybar/daq/fei4_raw_data.py
|
open_raw_data_file
|
def open_raw_data_file(filename, mode="w", title="", scan_parameters=None, socket_address=None):
'''Mimics pytables.open_file() and stores the configuration and run configuration
Returns:
RawDataFile Object
Examples:
with open_raw_data_file(filename = self.scan_data_filename, title=self.scan_id, scan_parameters=[scan_parameter]) as raw_data_file:
# do something here
raw_data_file.append(self.readout.data, scan_parameters={scan_parameter:scan_parameter_value})
'''
return RawDataFile(filename=filename, mode=mode, title=title, scan_parameters=scan_parameters, socket_address=socket_address)
|
python
|
def open_raw_data_file(filename, mode="w", title="", scan_parameters=None, socket_address=None):
'''Mimics pytables.open_file() and stores the configuration and run configuration
Returns:
RawDataFile Object
Examples:
with open_raw_data_file(filename = self.scan_data_filename, title=self.scan_id, scan_parameters=[scan_parameter]) as raw_data_file:
# do something here
raw_data_file.append(self.readout.data, scan_parameters={scan_parameter:scan_parameter_value})
'''
return RawDataFile(filename=filename, mode=mode, title=title, scan_parameters=scan_parameters, socket_address=socket_address)
|
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with open_raw_data_file(filename = self.scan_data_filename, title=self.scan_id, scan_parameters=[scan_parameter]) as raw_data_file:
# do something here
raw_data_file.append(self.readout.data, scan_parameters={scan_parameter:scan_parameter_value})
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/daq/fei4_raw_data.py#L47-L58
|
SiLab-Bonn/pyBAR
|
pybar/daq/fei4_raw_data.py
|
save_raw_data_from_data_queue
|
def save_raw_data_from_data_queue(data_queue, filename, mode='a', title='', scan_parameters=None): # mode="r+" to append data, raw_data_file_h5 must exist, "w" to overwrite raw_data_file_h5, "a" to append data, if raw_data_file_h5 does not exist it is created
'''Writing raw data file from data queue
If you need to write raw data once in a while this function may make it easy for you.
'''
if not scan_parameters:
scan_parameters = {}
with open_raw_data_file(filename, mode='a', title='', scan_parameters=list(dict.iterkeys(scan_parameters))) as raw_data_file:
raw_data_file.append(data_queue, scan_parameters=scan_parameters)
|
python
|
def save_raw_data_from_data_queue(data_queue, filename, mode='a', title='', scan_parameters=None): # mode="r+" to append data, raw_data_file_h5 must exist, "w" to overwrite raw_data_file_h5, "a" to append data, if raw_data_file_h5 does not exist it is created
'''Writing raw data file from data queue
If you need to write raw data once in a while this function may make it easy for you.
'''
if not scan_parameters:
scan_parameters = {}
with open_raw_data_file(filename, mode='a', title='', scan_parameters=list(dict.iterkeys(scan_parameters))) as raw_data_file:
raw_data_file.append(data_queue, scan_parameters=scan_parameters)
|
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|
[
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/daq/fei4_raw_data.py#L240-L248
|
SiLab-Bonn/pyBAR
|
pybar/scans/tune_fei4.py
|
Fei4Tuning.scan
|
def scan(self):
'''Metascript that calls other scripts to tune the FE.
Parameters
----------
cfg_name : string
Name of the config to be created. This config holds the tuning results.
target_threshold : int
The target threshold value in PlsrDAC.
target_charge : int
The target charge in PlsrDAC value to tune to.
target_tot : float
The target tot value to tune to.
global_iterations : int
Defines how often global threshold (GDAC) / global feedback (PrmpVbpf) current tuning is repeated.
-1 or None: Global tuning is disabled
0: Only global threshold tuning
1: GDAC -> PrmpVbpf -> GDAC
2: GDAC -> PrmpVbpf -> GDAC -> PrmpVbpf -> GDAC
...
local_iterations : int
Defines how often local threshold (TDAC) / feedback current (FDAC) tuning is repeated.
-1 or None: Local tuning is disabled
0: Only local threshold tuning
1: TDAC -> FDAC -> TDAC
2: TDAC -> FDAC -> TDAC -> FDAC -> TDAC
...
'''
for iteration in range(0, self.global_iterations): # tune iteratively with decreasing range to save time
if self.stop_run.is_set():
break
logging.info("Global tuning step %d / %d", iteration + 1, self.global_iterations)
self.set_scan_parameters(global_step=self.scan_parameters.global_step + 1)
GdacTuning.scan(self)
commands = []
commands.extend(self.register.get_commands("ConfMode"))
commands.extend(self.register.get_commands("WrRegister", name=["Vthin_AltCoarse", "Vthin_AltFine"]))
commands.extend(self.register.get_commands("RunMode"))
self.register_utils.send_commands(commands)
if self.stop_run.is_set():
break
self.set_scan_parameters(global_step=self.scan_parameters.global_step + 1)
FeedbackTuning.scan(self)
commands = []
commands.extend(self.register.get_commands("ConfMode"))
commands.extend(self.register.get_commands("WrRegister", name=["PrmpVbpf"]))
commands.extend(self.register.get_commands("RunMode"))
self.register_utils.send_commands(commands)
if self.global_iterations >= 0 and not self.stop_run.is_set():
self.set_scan_parameters(global_step=self.scan_parameters.global_step + 1)
GdacTuning.scan(self)
commands = []
commands.extend(self.register.get_commands("ConfMode"))
commands.extend(self.register.get_commands("WrRegister", name=["Vthin_AltCoarse", "Vthin_AltFine"]))
commands.extend(self.register.get_commands("RunMode"))
self.register_utils.send_commands(commands)
Vthin_AC = self.register.get_global_register_value("Vthin_AltCoarse")
Vthin_AF = self.register.get_global_register_value("Vthin_AltFine")
PrmpVbpf = self.register.get_global_register_value("PrmpVbpf")
logging.info("Results of global threshold tuning: Vthin_AltCoarse / Vthin_AltFine = %d / %d", Vthin_AC, Vthin_AF)
logging.info("Results of global feedback tuning: PrmpVbpf = %d", PrmpVbpf)
for iteration in range(0, self.local_iterations):
if self.stop_run.is_set():
break
logging.info("Local tuning step %d / %d", iteration + 1, self.local_iterations)
self.set_scan_parameters(local_step=self.scan_parameters.local_step + 1)
TdacTuning.scan(self)
commands = []
commands.extend(self.register.get_commands("ConfMode"))
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, name="TDAC"))
commands.extend(self.register.get_commands("RunMode"))
self.register_utils.send_commands(commands)
if self.stop_run.is_set():
break
self.set_scan_parameters(local_step=self.scan_parameters.local_step + 1)
FdacTuning.scan(self)
commands = []
commands.extend(self.register.get_commands("ConfMode"))
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, name="FDAC"))
commands.extend(self.register.get_commands("RunMode"))
self.register_utils.send_commands(commands)
if self.local_iterations >= 0 and not self.stop_run.is_set():
self.set_scan_parameters(local_step=self.scan_parameters.local_step + 1)
TdacTuning.scan(self)
commands = []
commands.extend(self.register.get_commands("ConfMode"))
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, name="TDAC"))
commands.extend(self.register.get_commands("RunMode"))
self.register_utils.send_commands(commands)
|
python
|
def scan(self):
'''Metascript that calls other scripts to tune the FE.
Parameters
----------
cfg_name : string
Name of the config to be created. This config holds the tuning results.
target_threshold : int
The target threshold value in PlsrDAC.
target_charge : int
The target charge in PlsrDAC value to tune to.
target_tot : float
The target tot value to tune to.
global_iterations : int
Defines how often global threshold (GDAC) / global feedback (PrmpVbpf) current tuning is repeated.
-1 or None: Global tuning is disabled
0: Only global threshold tuning
1: GDAC -> PrmpVbpf -> GDAC
2: GDAC -> PrmpVbpf -> GDAC -> PrmpVbpf -> GDAC
...
local_iterations : int
Defines how often local threshold (TDAC) / feedback current (FDAC) tuning is repeated.
-1 or None: Local tuning is disabled
0: Only local threshold tuning
1: TDAC -> FDAC -> TDAC
2: TDAC -> FDAC -> TDAC -> FDAC -> TDAC
...
'''
for iteration in range(0, self.global_iterations): # tune iteratively with decreasing range to save time
if self.stop_run.is_set():
break
logging.info("Global tuning step %d / %d", iteration + 1, self.global_iterations)
self.set_scan_parameters(global_step=self.scan_parameters.global_step + 1)
GdacTuning.scan(self)
commands = []
commands.extend(self.register.get_commands("ConfMode"))
commands.extend(self.register.get_commands("WrRegister", name=["Vthin_AltCoarse", "Vthin_AltFine"]))
commands.extend(self.register.get_commands("RunMode"))
self.register_utils.send_commands(commands)
if self.stop_run.is_set():
break
self.set_scan_parameters(global_step=self.scan_parameters.global_step + 1)
FeedbackTuning.scan(self)
commands = []
commands.extend(self.register.get_commands("ConfMode"))
commands.extend(self.register.get_commands("WrRegister", name=["PrmpVbpf"]))
commands.extend(self.register.get_commands("RunMode"))
self.register_utils.send_commands(commands)
if self.global_iterations >= 0 and not self.stop_run.is_set():
self.set_scan_parameters(global_step=self.scan_parameters.global_step + 1)
GdacTuning.scan(self)
commands = []
commands.extend(self.register.get_commands("ConfMode"))
commands.extend(self.register.get_commands("WrRegister", name=["Vthin_AltCoarse", "Vthin_AltFine"]))
commands.extend(self.register.get_commands("RunMode"))
self.register_utils.send_commands(commands)
Vthin_AC = self.register.get_global_register_value("Vthin_AltCoarse")
Vthin_AF = self.register.get_global_register_value("Vthin_AltFine")
PrmpVbpf = self.register.get_global_register_value("PrmpVbpf")
logging.info("Results of global threshold tuning: Vthin_AltCoarse / Vthin_AltFine = %d / %d", Vthin_AC, Vthin_AF)
logging.info("Results of global feedback tuning: PrmpVbpf = %d", PrmpVbpf)
for iteration in range(0, self.local_iterations):
if self.stop_run.is_set():
break
logging.info("Local tuning step %d / %d", iteration + 1, self.local_iterations)
self.set_scan_parameters(local_step=self.scan_parameters.local_step + 1)
TdacTuning.scan(self)
commands = []
commands.extend(self.register.get_commands("ConfMode"))
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, name="TDAC"))
commands.extend(self.register.get_commands("RunMode"))
self.register_utils.send_commands(commands)
if self.stop_run.is_set():
break
self.set_scan_parameters(local_step=self.scan_parameters.local_step + 1)
FdacTuning.scan(self)
commands = []
commands.extend(self.register.get_commands("ConfMode"))
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, name="FDAC"))
commands.extend(self.register.get_commands("RunMode"))
self.register_utils.send_commands(commands)
if self.local_iterations >= 0 and not self.stop_run.is_set():
self.set_scan_parameters(local_step=self.scan_parameters.local_step + 1)
TdacTuning.scan(self)
commands = []
commands.extend(self.register.get_commands("ConfMode"))
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, name="TDAC"))
commands.extend(self.register.get_commands("RunMode"))
self.register_utils.send_commands(commands)
|
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] |
Metascript that calls other scripts to tune the FE.
Parameters
----------
cfg_name : string
Name of the config to be created. This config holds the tuning results.
target_threshold : int
The target threshold value in PlsrDAC.
target_charge : int
The target charge in PlsrDAC value to tune to.
target_tot : float
The target tot value to tune to.
global_iterations : int
Defines how often global threshold (GDAC) / global feedback (PrmpVbpf) current tuning is repeated.
-1 or None: Global tuning is disabled
0: Only global threshold tuning
1: GDAC -> PrmpVbpf -> GDAC
2: GDAC -> PrmpVbpf -> GDAC -> PrmpVbpf -> GDAC
...
local_iterations : int
Defines how often local threshold (TDAC) / feedback current (FDAC) tuning is repeated.
-1 or None: Local tuning is disabled
0: Only local threshold tuning
1: TDAC -> FDAC -> TDAC
2: TDAC -> FDAC -> TDAC -> FDAC -> TDAC
...
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/scans/tune_fei4.py#L92-L184
|
SiLab-Bonn/pyBAR
|
pybar/analysis/plotting/plotting.py
|
plot_linear_relation
|
def plot_linear_relation(x, y, x_err=None, y_err=None, title=None, point_label=None, legend=None, plot_range=None, plot_range_y=None, x_label=None, y_label=None, y_2_label=None, log_x=False, log_y=False, size=None, filename=None):
''' Takes point data (x,y) with errors(x,y) and fits a straight line. The deviation to this line is also plotted, showing the offset.
Parameters
----------
x, y, x_err, y_err: iterable
filename: string, PdfPages object or None
PdfPages file object: plot is appended to the pdf
string: new plot file with the given filename is created
None: the plot is printed to screen
'''
fig = Figure()
FigureCanvas(fig)
ax = fig.add_subplot(111)
if x_err is not None:
x_err = [x_err, x_err]
if y_err is not None:
y_err = [y_err, y_err]
ax.set_title(title)
if y_label is not None:
ax.set_ylabel(y_label)
if log_x:
ax.set_xscale('log')
if log_y:
ax.set_yscale('log')
if plot_range:
ax.set_xlim((min(plot_range), max(plot_range)))
if plot_range_y:
ax.set_ylim((min(plot_range_y), max(plot_range_y)))
if legend:
fig.legend(legend, 0)
ax.grid(True)
ax.errorbar(x, y, xerr=x_err, yerr=y_err, fmt='o', color='black') # plot points
# label points if needed
if point_label is not None:
for X, Y, Z in zip(x, y, point_label):
ax.annotate('{}'.format(Z), xy=(X, Y), xytext=(-5, 5), ha='right', textcoords='offset points')
line_fit, _ = np.polyfit(x, y, 1, full=False, cov=True)
fit_fn = np.poly1d(line_fit)
ax.plot(x, fit_fn(x), '-', lw=2, color='gray')
setp(ax.get_xticklabels(), visible=False) # remove ticks at common border of both plots
divider = make_axes_locatable(ax)
ax_bottom_plot = divider.append_axes("bottom", 2.0, pad=0.0, sharex=ax)
ax_bottom_plot.bar(x, y - fit_fn(x), align='center', width=np.amin(np.diff(x)) / 2, color='gray')
# plot(x, y - fit_fn(x))
ax_bottom_plot.grid(True)
if x_label is not None:
ax.set_xlabel(x_label)
if y_2_label is not None:
ax.set_ylabel(y_2_label)
ax.set_ylim((-np.amax(np.abs(y - fit_fn(x)))), (np.amax(np.abs(y - fit_fn(x)))))
ax.plot(ax.set_xlim(), [0, 0], '-', color='black')
setp(ax_bottom_plot.get_yticklabels()[-2:-1], visible=False)
if size is not None:
fig.set_size_inches(size)
if not filename:
fig.show()
elif isinstance(filename, PdfPages):
filename.savefig(fig)
elif filename:
fig.savefig(filename, bbox_inches='tight')
return fig
|
python
|
def plot_linear_relation(x, y, x_err=None, y_err=None, title=None, point_label=None, legend=None, plot_range=None, plot_range_y=None, x_label=None, y_label=None, y_2_label=None, log_x=False, log_y=False, size=None, filename=None):
''' Takes point data (x,y) with errors(x,y) and fits a straight line. The deviation to this line is also plotted, showing the offset.
Parameters
----------
x, y, x_err, y_err: iterable
filename: string, PdfPages object or None
PdfPages file object: plot is appended to the pdf
string: new plot file with the given filename is created
None: the plot is printed to screen
'''
fig = Figure()
FigureCanvas(fig)
ax = fig.add_subplot(111)
if x_err is not None:
x_err = [x_err, x_err]
if y_err is not None:
y_err = [y_err, y_err]
ax.set_title(title)
if y_label is not None:
ax.set_ylabel(y_label)
if log_x:
ax.set_xscale('log')
if log_y:
ax.set_yscale('log')
if plot_range:
ax.set_xlim((min(plot_range), max(plot_range)))
if plot_range_y:
ax.set_ylim((min(plot_range_y), max(plot_range_y)))
if legend:
fig.legend(legend, 0)
ax.grid(True)
ax.errorbar(x, y, xerr=x_err, yerr=y_err, fmt='o', color='black') # plot points
# label points if needed
if point_label is not None:
for X, Y, Z in zip(x, y, point_label):
ax.annotate('{}'.format(Z), xy=(X, Y), xytext=(-5, 5), ha='right', textcoords='offset points')
line_fit, _ = np.polyfit(x, y, 1, full=False, cov=True)
fit_fn = np.poly1d(line_fit)
ax.plot(x, fit_fn(x), '-', lw=2, color='gray')
setp(ax.get_xticklabels(), visible=False) # remove ticks at common border of both plots
divider = make_axes_locatable(ax)
ax_bottom_plot = divider.append_axes("bottom", 2.0, pad=0.0, sharex=ax)
ax_bottom_plot.bar(x, y - fit_fn(x), align='center', width=np.amin(np.diff(x)) / 2, color='gray')
# plot(x, y - fit_fn(x))
ax_bottom_plot.grid(True)
if x_label is not None:
ax.set_xlabel(x_label)
if y_2_label is not None:
ax.set_ylabel(y_2_label)
ax.set_ylim((-np.amax(np.abs(y - fit_fn(x)))), (np.amax(np.abs(y - fit_fn(x)))))
ax.plot(ax.set_xlim(), [0, 0], '-', color='black')
setp(ax_bottom_plot.get_yticklabels()[-2:-1], visible=False)
if size is not None:
fig.set_size_inches(size)
if not filename:
fig.show()
elif isinstance(filename, PdfPages):
filename.savefig(fig)
elif filename:
fig.savefig(filename, bbox_inches='tight')
return fig
|
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Takes point data (x,y) with errors(x,y) and fits a straight line. The deviation to this line is also plotted, showing the offset.
Parameters
----------
x, y, x_err, y_err: iterable
filename: string, PdfPages object or None
PdfPages file object: plot is appended to the pdf
string: new plot file with the given filename is created
None: the plot is printed to screen
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/plotting/plotting.py#L52-L121
|
SiLab-Bonn/pyBAR
|
pybar/analysis/plotting/plotting.py
|
plot_profile_histogram
|
def plot_profile_histogram(x, y, n_bins=100, title=None, x_label=None, y_label=None, log_y=False, filename=None):
'''Takes 2D point data (x,y) and creates a profile histogram similar to the TProfile in ROOT. It calculates
the y mean for every bin at the bin center and gives the y mean error as error bars.
Parameters
----------
x : array like
data x positions
y : array like
data y positions
n_bins : int
the number of bins used to create the histogram
'''
if len(x) != len(y):
raise ValueError('x and y dimensions have to be the same')
n, bin_edges = np.histogram(x, bins=n_bins) # needed to calculate the number of points per bin
sy = np.histogram(x, bins=n_bins, weights=y)[0] # the sum of the bin values
sy2 = np.histogram(x, bins=n_bins, weights=y * y)[0] # the quadratic sum of the bin values
bin_centers = (bin_edges[1:] + bin_edges[:-1]) / 2 # calculate the bin center for all bins
mean = sy / n # calculate the mean of all bins
std = np.sqrt((sy2 / n - mean * mean)) # TODO: not understood, need check if this is really the standard deviation
# std_mean = np.sqrt((sy2 - 2 * mean * sy + mean * mean) / (1*(n - 1))) # this should be the formular ?!
std_mean = std / np.sqrt((n - 1))
mean[np.isnan(mean)] = 0.0
std_mean[np.isnan(std_mean)] = 0.0
fig = Figure()
FigureCanvas(fig)
ax = fig.add_subplot(111)
ax.errorbar(bin_centers, mean, yerr=std_mean, fmt='o')
ax.set_title(title)
if x_label is not None:
ax.set_xlabel(x_label)
if y_label is not None:
ax.set_ylabel(y_label)
if log_y:
ax.yscale('log')
ax.grid(True)
if not filename:
fig.show()
elif isinstance(filename, PdfPages):
filename.savefig(fig)
else:
fig.savefig(filename)
|
python
|
def plot_profile_histogram(x, y, n_bins=100, title=None, x_label=None, y_label=None, log_y=False, filename=None):
'''Takes 2D point data (x,y) and creates a profile histogram similar to the TProfile in ROOT. It calculates
the y mean for every bin at the bin center and gives the y mean error as error bars.
Parameters
----------
x : array like
data x positions
y : array like
data y positions
n_bins : int
the number of bins used to create the histogram
'''
if len(x) != len(y):
raise ValueError('x and y dimensions have to be the same')
n, bin_edges = np.histogram(x, bins=n_bins) # needed to calculate the number of points per bin
sy = np.histogram(x, bins=n_bins, weights=y)[0] # the sum of the bin values
sy2 = np.histogram(x, bins=n_bins, weights=y * y)[0] # the quadratic sum of the bin values
bin_centers = (bin_edges[1:] + bin_edges[:-1]) / 2 # calculate the bin center for all bins
mean = sy / n # calculate the mean of all bins
std = np.sqrt((sy2 / n - mean * mean)) # TODO: not understood, need check if this is really the standard deviation
# std_mean = np.sqrt((sy2 - 2 * mean * sy + mean * mean) / (1*(n - 1))) # this should be the formular ?!
std_mean = std / np.sqrt((n - 1))
mean[np.isnan(mean)] = 0.0
std_mean[np.isnan(std_mean)] = 0.0
fig = Figure()
FigureCanvas(fig)
ax = fig.add_subplot(111)
ax.errorbar(bin_centers, mean, yerr=std_mean, fmt='o')
ax.set_title(title)
if x_label is not None:
ax.set_xlabel(x_label)
if y_label is not None:
ax.set_ylabel(y_label)
if log_y:
ax.yscale('log')
ax.grid(True)
if not filename:
fig.show()
elif isinstance(filename, PdfPages):
filename.savefig(fig)
else:
fig.savefig(filename)
|
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x : array like
data x positions
y : array like
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n_bins : int
the number of bins used to create the histogram
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/plotting/plotting.py#L241-L284
|
SiLab-Bonn/pyBAR
|
pybar/analysis/plotting/plotting.py
|
round_to_multiple
|
def round_to_multiple(number, multiple):
'''Rounding up to the nearest multiple of any positive integer
Parameters
----------
number : int, float
Input number.
multiple : int
Round up to multiple of multiple. Will be converted to int. Must not be equal zero.
Returns
-------
ceil_mod_number : int
Rounded up number.
Example
-------
round_to_multiple(maximum, math.floor(math.log10(maximum)))
'''
multiple = int(multiple)
if multiple == 0:
multiple = 1
ceil_mod_number = number - number % (-multiple)
return int(ceil_mod_number)
|
python
|
def round_to_multiple(number, multiple):
'''Rounding up to the nearest multiple of any positive integer
Parameters
----------
number : int, float
Input number.
multiple : int
Round up to multiple of multiple. Will be converted to int. Must not be equal zero.
Returns
-------
ceil_mod_number : int
Rounded up number.
Example
-------
round_to_multiple(maximum, math.floor(math.log10(maximum)))
'''
multiple = int(multiple)
if multiple == 0:
multiple = 1
ceil_mod_number = number - number % (-multiple)
return int(ceil_mod_number)
|
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Rounding up to the nearest multiple of any positive integer
Parameters
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number : int, float
Input number.
multiple : int
Round up to multiple of multiple. Will be converted to int. Must not be equal zero.
Returns
-------
ceil_mod_number : int
Rounded up number.
Example
-------
round_to_multiple(maximum, math.floor(math.log10(maximum)))
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/plotting/plotting.py#L352-L374
|
SiLab-Bonn/pyBAR
|
pybar/analysis/plotting/plotting.py
|
hist_quantiles
|
def hist_quantiles(hist, prob=(0.05, 0.95), return_indices=False, copy=True):
'''Calculate quantiles from histograms, cuts off hist below and above given quantile. This function will not cut off more than the given values.
Parameters
----------
hist : array_like, iterable
Input histogram with dimension at most 1.
prob : float, list, tuple
List of quantiles to compute. Upper and lower limit. From 0 to 1. Default is 0.05 and 0.95.
return_indices : bool, optional
If true, return the indices of the hist.
copy : bool, optional
Whether to copy the input data (True), or to use a reference instead. Default is False.
Returns
-------
masked_hist : masked_array
Hist with masked elements.
masked_hist : masked_array, tuple
Hist with masked elements and indices.
'''
# make np array
hist_t = np.array(hist)
# calculate cumulative distribution
cdf = np.cumsum(hist_t)
# copy, convert and normalize
if cdf[-1] == 0:
normcdf = cdf.astype('float')
else:
normcdf = cdf.astype('float') / cdf[-1]
# calculate unique values from cumulative distribution and their indices
unormcdf, indices = np.unique(normcdf, return_index=True)
# calculate limits
try:
hp = np.where(unormcdf > prob[1])[0][0]
lp = np.where(unormcdf >= prob[0])[0][0]
except IndexError:
hp_index = hist_t.shape[0]
lp_index = 0
else:
hp_index = indices[hp]
lp_index = indices[lp]
# copy and create ma
masked_hist = np.ma.array(hist, copy=copy, mask=True)
masked_hist.mask[lp_index:hp_index + 1] = False
if return_indices:
return masked_hist, (lp_index, hp_index)
else:
return masked_hist
|
python
|
def hist_quantiles(hist, prob=(0.05, 0.95), return_indices=False, copy=True):
'''Calculate quantiles from histograms, cuts off hist below and above given quantile. This function will not cut off more than the given values.
Parameters
----------
hist : array_like, iterable
Input histogram with dimension at most 1.
prob : float, list, tuple
List of quantiles to compute. Upper and lower limit. From 0 to 1. Default is 0.05 and 0.95.
return_indices : bool, optional
If true, return the indices of the hist.
copy : bool, optional
Whether to copy the input data (True), or to use a reference instead. Default is False.
Returns
-------
masked_hist : masked_array
Hist with masked elements.
masked_hist : masked_array, tuple
Hist with masked elements and indices.
'''
# make np array
hist_t = np.array(hist)
# calculate cumulative distribution
cdf = np.cumsum(hist_t)
# copy, convert and normalize
if cdf[-1] == 0:
normcdf = cdf.astype('float')
else:
normcdf = cdf.astype('float') / cdf[-1]
# calculate unique values from cumulative distribution and their indices
unormcdf, indices = np.unique(normcdf, return_index=True)
# calculate limits
try:
hp = np.where(unormcdf > prob[1])[0][0]
lp = np.where(unormcdf >= prob[0])[0][0]
except IndexError:
hp_index = hist_t.shape[0]
lp_index = 0
else:
hp_index = indices[hp]
lp_index = indices[lp]
# copy and create ma
masked_hist = np.ma.array(hist, copy=copy, mask=True)
masked_hist.mask[lp_index:hp_index + 1] = False
if return_indices:
return masked_hist, (lp_index, hp_index)
else:
return masked_hist
|
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Calculate quantiles from histograms, cuts off hist below and above given quantile. This function will not cut off more than the given values.
Parameters
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hist : array_like, iterable
Input histogram with dimension at most 1.
prob : float, list, tuple
List of quantiles to compute. Upper and lower limit. From 0 to 1. Default is 0.05 and 0.95.
return_indices : bool, optional
If true, return the indices of the hist.
copy : bool, optional
Whether to copy the input data (True), or to use a reference instead. Default is False.
Returns
-------
masked_hist : masked_array
Hist with masked elements.
masked_hist : masked_array, tuple
Hist with masked elements and indices.
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/plotting/plotting.py#L831-L879
|
SiLab-Bonn/pyBAR
|
pybar/analysis/plotting/plotting.py
|
hist_last_nonzero
|
def hist_last_nonzero(hist, return_index=False, copy=True):
'''Find the last nonzero index and mask the remaining entries.
Parameters
----------
hist : array_like, iterable
Input histogram with dimension at most 1.
return_index : bool, optional
If true, return the index.
copy : bool, optional
Whether to copy the input data (True), or to use a reference instead. Default is False.
Returns
-------
masked_hist : masked_array
Hist with masked elements.
masked_hist : masked_array, tuple
Hist with masked elements and index of the element after the last nonzero value.
'''
# make np array
hist_t = np.array(hist)
index = (np.where(hist_t)[-1][-1] + 1) if np.sum(hist_t) > 1 else hist_t.shape[0]
# copy and create ma
masked_hist = np.ma.array(hist, copy=copy, mask=True)
masked_hist.mask[index:] = False
if return_index:
return masked_hist, index
else:
return masked_hist
|
python
|
def hist_last_nonzero(hist, return_index=False, copy=True):
'''Find the last nonzero index and mask the remaining entries.
Parameters
----------
hist : array_like, iterable
Input histogram with dimension at most 1.
return_index : bool, optional
If true, return the index.
copy : bool, optional
Whether to copy the input data (True), or to use a reference instead. Default is False.
Returns
-------
masked_hist : masked_array
Hist with masked elements.
masked_hist : masked_array, tuple
Hist with masked elements and index of the element after the last nonzero value.
'''
# make np array
hist_t = np.array(hist)
index = (np.where(hist_t)[-1][-1] + 1) if np.sum(hist_t) > 1 else hist_t.shape[0]
# copy and create ma
masked_hist = np.ma.array(hist, copy=copy, mask=True)
masked_hist.mask[index:] = False
if return_index:
return masked_hist, index
else:
return masked_hist
|
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copy : bool, optional
Whether to copy the input data (True), or to use a reference instead. Default is False.
Returns
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masked_hist : masked_array
Hist with masked elements.
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Hist with masked elements and index of the element after the last nonzero value.
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/plotting/plotting.py#L882-L910
|
SiLab-Bonn/pyBAR
|
pybar/daq/fifo_readout.py
|
FifoReadout.readout
|
def readout(self, fifo, no_data_timeout=None):
'''Readout thread continuously reading FIFO.
Readout thread, which uses read_raw_data_from_fifo() and appends data to self._fifo_data_deque (collection.deque).
'''
logging.info('Starting readout thread for %s', fifo)
time_last_data = time()
time_wait = 0.0
empty_reads = 0
while not self.force_stop[fifo].wait(time_wait if time_wait >= 0.0 else 0.0):
time_read = time()
try:
if no_data_timeout and time_last_data + no_data_timeout < get_float_time():
raise NoDataTimeout('Received no data for %0.1f second(s) from %s' % (no_data_timeout, fifo))
raw_data = self.read_raw_data_from_fifo(fifo)
except NoDataTimeout:
no_data_timeout = None # raise exception only once
if self.errback:
self.errback(sys.exc_info())
else:
raise
except Exception:
if self.errback:
self.errback(sys.exc_info())
else:
raise
if self.stop_readout.is_set(): # in case of a exception, break immediately
break
else:
n_data_words = raw_data.shape[0]
if n_data_words > 0:
time_last_data = time()
empty_reads = 0
time_start_read, time_stop_read = self.update_timestamp(fifo)
status = 0
self._fifo_data_deque[fifo].append((raw_data, time_start_read, time_stop_read, status))
with self._fifo_conditions[fifo]:
self._fifo_conditions[fifo].notify_all()
elif self.stop_readout.is_set():
if empty_reads == self._n_empty_reads:
break
else:
empty_reads += 1
finally:
# ensure that the readout interval does not depend on the processing time of the data
# and stays more or less constant over time
time_wait = self.readout_interval - (time() - time_read)
self._fifo_data_deque[fifo].append(None) # last item, None will stop worker
with self._fifo_conditions[fifo]:
self._fifo_conditions[fifo].notify_all()
logging.info('Stopping readout thread for %s', fifo)
|
python
|
def readout(self, fifo, no_data_timeout=None):
'''Readout thread continuously reading FIFO.
Readout thread, which uses read_raw_data_from_fifo() and appends data to self._fifo_data_deque (collection.deque).
'''
logging.info('Starting readout thread for %s', fifo)
time_last_data = time()
time_wait = 0.0
empty_reads = 0
while not self.force_stop[fifo].wait(time_wait if time_wait >= 0.0 else 0.0):
time_read = time()
try:
if no_data_timeout and time_last_data + no_data_timeout < get_float_time():
raise NoDataTimeout('Received no data for %0.1f second(s) from %s' % (no_data_timeout, fifo))
raw_data = self.read_raw_data_from_fifo(fifo)
except NoDataTimeout:
no_data_timeout = None # raise exception only once
if self.errback:
self.errback(sys.exc_info())
else:
raise
except Exception:
if self.errback:
self.errback(sys.exc_info())
else:
raise
if self.stop_readout.is_set(): # in case of a exception, break immediately
break
else:
n_data_words = raw_data.shape[0]
if n_data_words > 0:
time_last_data = time()
empty_reads = 0
time_start_read, time_stop_read = self.update_timestamp(fifo)
status = 0
self._fifo_data_deque[fifo].append((raw_data, time_start_read, time_stop_read, status))
with self._fifo_conditions[fifo]:
self._fifo_conditions[fifo].notify_all()
elif self.stop_readout.is_set():
if empty_reads == self._n_empty_reads:
break
else:
empty_reads += 1
finally:
# ensure that the readout interval does not depend on the processing time of the data
# and stays more or less constant over time
time_wait = self.readout_interval - (time() - time_read)
self._fifo_data_deque[fifo].append(None) # last item, None will stop worker
with self._fifo_conditions[fifo]:
self._fifo_conditions[fifo].notify_all()
logging.info('Stopping readout thread for %s', fifo)
|
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"fifo",
")"
] |
Readout thread continuously reading FIFO.
Readout thread, which uses read_raw_data_from_fifo() and appends data to self._fifo_data_deque (collection.deque).
|
[
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/daq/fifo_readout.py#L270-L320
|
SiLab-Bonn/pyBAR
|
pybar/daq/fifo_readout.py
|
FifoReadout.worker
|
def worker(self, fifo):
'''Worker thread continuously filtering and converting data when data becomes available.
'''
logging.debug('Starting worker thread for %s', fifo)
self._fifo_conditions[fifo].acquire()
while True:
try:
data_tuple = self._fifo_data_deque[fifo].popleft()
except IndexError:
self._fifo_conditions[fifo].wait(self.readout_interval) # sleep a little bit, reducing CPU usage
else:
if data_tuple is None: # if None then exit
break
else:
for index, (filter_func, converter_func, fifo_select) in enumerate(izip(self.filter_func, self.converter_func, self.fifo_select)):
if fifo_select is None or fifo_select == fifo:
# filter and do the conversion
converted_data_tuple = convert_data_iterable((data_tuple,), filter_func=filter_func, converter_func=converter_func)[0]
n_data_words = converted_data_tuple[0].shape[0]
with self.data_words_per_second_lock:
self._words_per_read[index].append((n_data_words, converted_data_tuple[1], converted_data_tuple[2]))
self._data_deque[index].append(converted_data_tuple)
with self._data_conditions[index]:
self._data_conditions[index].notify_all()
for index, fifo_select in enumerate(self.fifo_select):
if fifo_select is None or fifo_select == fifo:
self._data_deque[index].append(None)
with self._data_conditions[index]:
self._data_conditions[index].notify_all()
self._fifo_conditions[fifo].release()
logging.debug('Stopping worker thread for %s', fifo)
|
python
|
def worker(self, fifo):
'''Worker thread continuously filtering and converting data when data becomes available.
'''
logging.debug('Starting worker thread for %s', fifo)
self._fifo_conditions[fifo].acquire()
while True:
try:
data_tuple = self._fifo_data_deque[fifo].popleft()
except IndexError:
self._fifo_conditions[fifo].wait(self.readout_interval) # sleep a little bit, reducing CPU usage
else:
if data_tuple is None: # if None then exit
break
else:
for index, (filter_func, converter_func, fifo_select) in enumerate(izip(self.filter_func, self.converter_func, self.fifo_select)):
if fifo_select is None or fifo_select == fifo:
# filter and do the conversion
converted_data_tuple = convert_data_iterable((data_tuple,), filter_func=filter_func, converter_func=converter_func)[0]
n_data_words = converted_data_tuple[0].shape[0]
with self.data_words_per_second_lock:
self._words_per_read[index].append((n_data_words, converted_data_tuple[1], converted_data_tuple[2]))
self._data_deque[index].append(converted_data_tuple)
with self._data_conditions[index]:
self._data_conditions[index].notify_all()
for index, fifo_select in enumerate(self.fifo_select):
if fifo_select is None or fifo_select == fifo:
self._data_deque[index].append(None)
with self._data_conditions[index]:
self._data_conditions[index].notify_all()
self._fifo_conditions[fifo].release()
logging.debug('Stopping worker thread for %s', fifo)
|
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|
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"Worker",
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"when",
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"available",
"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/daq/fifo_readout.py#L322-L352
|
SiLab-Bonn/pyBAR
|
pybar/daq/fifo_readout.py
|
FifoReadout.writer
|
def writer(self, index, no_data_timeout=None):
'''Writer thread continuously calling callback function for writing data when data becomes available.
'''
is_fe_data_header = logical_and(is_fe_word, is_data_header)
logging.debug('Starting writer thread with index %d', index)
self._data_conditions[index].acquire()
time_last_data = time()
time_write = time()
converted_data_tuple_list = [None] * len(self.filter_func) # callback function gets a list of lists of tuples
while True:
try:
if no_data_timeout and time_last_data + no_data_timeout < time():
raise NoDataTimeout('Received no data for %0.1f second(s) for writer thread with index %d' % (no_data_timeout, index))
converted_data_tuple = self._data_deque[index].popleft()
except NoDataTimeout: # no data timeout
no_data_timeout = None # raise exception only once
if self.errback:
self.errback(sys.exc_info())
else:
raise
except IndexError: # no data in queue
self._data_conditions[index].wait(self.readout_interval) # sleep a little bit, reducing CPU usage
else:
if converted_data_tuple is None: # if None then write and exit
if self.callback and any(converted_data_tuple_list):
try:
self.callback(converted_data_tuple_list)
except Exception:
self.errback(sys.exc_info())
break
else:
if no_data_timeout and np.any(is_fe_data_header(converted_data_tuple[0])): # check for FEI4 data words
time_last_data = time()
if converted_data_tuple_list[index]:
converted_data_tuple_list[index].append(converted_data_tuple)
else:
converted_data_tuple_list[index] = [converted_data_tuple] # adding iterable
if self.fill_buffer:
self._data_buffer[index].append(converted_data_tuple)
# check if calling the callback function is about time
if self.callback and any(converted_data_tuple_list) and ((self.write_interval and time() - time_write >= self.write_interval) or not self.write_interval):
try:
self.callback(converted_data_tuple_list) # callback function gets a list of lists of tuples
except Exception:
self.errback(sys.exc_info())
else:
converted_data_tuple_list = [None] * len(self.filter_func)
time_write = time() # update last write timestamp
self._data_conditions[index].release()
logging.debug('Stopping writer thread with index %d', index)
|
python
|
def writer(self, index, no_data_timeout=None):
'''Writer thread continuously calling callback function for writing data when data becomes available.
'''
is_fe_data_header = logical_and(is_fe_word, is_data_header)
logging.debug('Starting writer thread with index %d', index)
self._data_conditions[index].acquire()
time_last_data = time()
time_write = time()
converted_data_tuple_list = [None] * len(self.filter_func) # callback function gets a list of lists of tuples
while True:
try:
if no_data_timeout and time_last_data + no_data_timeout < time():
raise NoDataTimeout('Received no data for %0.1f second(s) for writer thread with index %d' % (no_data_timeout, index))
converted_data_tuple = self._data_deque[index].popleft()
except NoDataTimeout: # no data timeout
no_data_timeout = None # raise exception only once
if self.errback:
self.errback(sys.exc_info())
else:
raise
except IndexError: # no data in queue
self._data_conditions[index].wait(self.readout_interval) # sleep a little bit, reducing CPU usage
else:
if converted_data_tuple is None: # if None then write and exit
if self.callback and any(converted_data_tuple_list):
try:
self.callback(converted_data_tuple_list)
except Exception:
self.errback(sys.exc_info())
break
else:
if no_data_timeout and np.any(is_fe_data_header(converted_data_tuple[0])): # check for FEI4 data words
time_last_data = time()
if converted_data_tuple_list[index]:
converted_data_tuple_list[index].append(converted_data_tuple)
else:
converted_data_tuple_list[index] = [converted_data_tuple] # adding iterable
if self.fill_buffer:
self._data_buffer[index].append(converted_data_tuple)
# check if calling the callback function is about time
if self.callback and any(converted_data_tuple_list) and ((self.write_interval and time() - time_write >= self.write_interval) or not self.write_interval):
try:
self.callback(converted_data_tuple_list) # callback function gets a list of lists of tuples
except Exception:
self.errback(sys.exc_info())
else:
converted_data_tuple_list = [None] * len(self.filter_func)
time_write = time() # update last write timestamp
self._data_conditions[index].release()
logging.debug('Stopping writer thread with index %d', index)
|
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|
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] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/daq/fifo_readout.py#L354-L403
|
SiLab-Bonn/pyBAR
|
pybar/daq/fifo_readout.py
|
FifoReadout.get_data_from_buffer
|
def get_data_from_buffer(self, filter_func=None, converter_func=None):
'''Reads local data buffer and returns data and meta data list.
Returns
-------
data : list
List of data and meta data dicts.
'''
if self._is_running:
raise RuntimeError('Readout thread running')
if not self.fill_buffer:
logging.warning('Data buffer is not activated')
return [convert_data_iterable(data_iterable, filter_func=filter_func, converter_func=converter_func) for data_iterable in self._data_buffer]
|
python
|
def get_data_from_buffer(self, filter_func=None, converter_func=None):
'''Reads local data buffer and returns data and meta data list.
Returns
-------
data : list
List of data and meta data dicts.
'''
if self._is_running:
raise RuntimeError('Readout thread running')
if not self.fill_buffer:
logging.warning('Data buffer is not activated')
return [convert_data_iterable(data_iterable, filter_func=filter_func, converter_func=converter_func) for data_iterable in self._data_buffer]
|
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Reads local data buffer and returns data and meta data list.
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/daq/fifo_readout.py#L425-L437
|
SiLab-Bonn/pyBAR
|
pybar/daq/fifo_readout.py
|
FifoReadout.get_raw_data_from_buffer
|
def get_raw_data_from_buffer(self, filter_func=None, converter_func=None):
'''Reads local data buffer and returns raw data array.
Returns
-------
data : np.array
An array containing data words from the local data buffer.
'''
if self._is_running:
raise RuntimeError('Readout thread running')
if not self.fill_buffer:
logging.warning('Data buffer is not activated')
return [convert_data_array(data_array_from_data_iterable(data_iterable), filter_func=filter_func, converter_func=converter_func) for data_iterable in self._data_buffer]
|
python
|
def get_raw_data_from_buffer(self, filter_func=None, converter_func=None):
'''Reads local data buffer and returns raw data array.
Returns
-------
data : np.array
An array containing data words from the local data buffer.
'''
if self._is_running:
raise RuntimeError('Readout thread running')
if not self.fill_buffer:
logging.warning('Data buffer is not activated')
return [convert_data_array(data_array_from_data_iterable(data_iterable), filter_func=filter_func, converter_func=converter_func) for data_iterable in self._data_buffer]
|
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Reads local data buffer and returns raw data array.
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|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/daq/fifo_readout.py#L439-L451
|
SiLab-Bonn/pyBAR
|
pybar/daq/fifo_readout.py
|
FifoReadout.read_raw_data_from_fifo
|
def read_raw_data_from_fifo(self, fifo, filter_func=None, converter_func=None):
'''Reads FIFO data and returns raw data array.
Returns
-------
data : np.array
An array containing FIFO data words.
'''
return convert_data_array(self.dut[fifo].get_data(), filter_func=filter_func, converter_func=converter_func)
|
python
|
def read_raw_data_from_fifo(self, fifo, filter_func=None, converter_func=None):
'''Reads FIFO data and returns raw data array.
Returns
-------
data : np.array
An array containing FIFO data words.
'''
return convert_data_array(self.dut[fifo].get_data(), filter_func=filter_func, converter_func=converter_func)
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/daq/fifo_readout.py#L453-L461
|
SiLab-Bonn/pyBAR
|
pybar/utils/utils.py
|
get_item_from_queue
|
def get_item_from_queue(Q, timeout=0.01):
""" Attempts to retrieve an item from the queue Q. If Q is
empty, None is returned.
Blocks for 'timeout' seconds in case the queue is empty,
so don't use this method for speedy retrieval of multiple
items (use get_all_from_queue for that).
"""
try:
item = Q.get(True, 0.01)
except Queue.Empty:
return None
return item
|
python
|
def get_item_from_queue(Q, timeout=0.01):
""" Attempts to retrieve an item from the queue Q. If Q is
empty, None is returned.
Blocks for 'timeout' seconds in case the queue is empty,
so don't use this method for speedy retrieval of multiple
items (use get_all_from_queue for that).
"""
try:
item = Q.get(True, 0.01)
except Queue.Empty:
return None
return item
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/utils/utils.py#L38-L51
|
SiLab-Bonn/pyBAR
|
pybar/utils/utils.py
|
argmin_list
|
def argmin_list(seq, func):
""" Return a list of elements of seq[i] with the lowest
func(seq[i]) scores.
>>> argmin_list(['one', 'to', 'three', 'or'], len)
['to', 'or']
"""
best_score, best = func(seq[0]), []
for x in seq:
x_score = func(x)
if x_score < best_score:
best, best_score = [x], x_score
elif x_score == best_score:
best.append(x)
return best
|
python
|
def argmin_list(seq, func):
""" Return a list of elements of seq[i] with the lowest
func(seq[i]) scores.
>>> argmin_list(['one', 'to', 'three', 'or'], len)
['to', 'or']
"""
best_score, best = func(seq[0]), []
for x in seq:
x_score = func(x)
if x_score < best_score:
best, best_score = [x], x_score
elif x_score == best_score:
best.append(x)
return best
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/utils/utils.py#L62-L75
|
SiLab-Bonn/pyBAR
|
pybar/utils/utils.py
|
flatten_iterable
|
def flatten_iterable(iterable):
"""flatten iterable, but leaves out strings
[[[1, 2, 3], [4, 5]], 6] -> [1, 2, 3, 4, 5, 6]
"""
for item in iterable:
if isinstance(item, collections.Iterable) and not isinstance(item, basestring):
for sub in flatten_iterable(item):
yield sub
else:
yield item
|
python
|
def flatten_iterable(iterable):
"""flatten iterable, but leaves out strings
[[[1, 2, 3], [4, 5]], 6] -> [1, 2, 3, 4, 5, 6]
"""
for item in iterable:
if isinstance(item, collections.Iterable) and not isinstance(item, basestring):
for sub in flatten_iterable(item):
yield sub
else:
yield item
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/utils/utils.py#L198-L209
|
SiLab-Bonn/pyBAR
|
pybar/utils/utils.py
|
iterable
|
def iterable(item):
"""generate iterable from item, but leaves out strings
"""
if isinstance(item, collections.Iterable) and not isinstance(item, basestring):
return item
else:
return [item]
|
python
|
def iterable(item):
"""generate iterable from item, but leaves out strings
"""
if isinstance(item, collections.Iterable) and not isinstance(item, basestring):
return item
else:
return [item]
|
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generate iterable from item, but leaves out strings
|
[
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/utils/utils.py#L212-L219
|
SiLab-Bonn/pyBAR
|
pybar/utils/utils.py
|
natsorted
|
def natsorted(seq, cmp=natcmp):
"Returns a copy of seq, sorted by natural string sort."
import copy
temp = copy.copy(seq)
natsort(temp, cmp)
return temp
|
python
|
def natsorted(seq, cmp=natcmp):
"Returns a copy of seq, sorted by natural string sort."
import copy
temp = copy.copy(seq)
natsort(temp, cmp)
return temp
|
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|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/utils/utils.py#L258-L263
|
SiLab-Bonn/pyBAR
|
pybar/utils/utils.py
|
get_iso_time
|
def get_iso_time():
'''returns time as ISO string, mapping to and from datetime in ugly way
convert to string with str()
'''
t1 = time.time()
t2 = datetime.datetime.fromtimestamp(t1)
t4 = t2.__str__()
try:
t4a, t4b = t4.split(".", 1)
except ValueError:
t4a = t4
t4b = '000000'
t5 = datetime.datetime.strptime(t4a, "%Y-%m-%d %H:%M:%S")
ms = int(t4b.ljust(6, '0')[:6])
return t5.replace(microsecond=ms)
|
python
|
def get_iso_time():
'''returns time as ISO string, mapping to and from datetime in ugly way
convert to string with str()
'''
t1 = time.time()
t2 = datetime.datetime.fromtimestamp(t1)
t4 = t2.__str__()
try:
t4a, t4b = t4.split(".", 1)
except ValueError:
t4a = t4
t4b = '000000'
t5 = datetime.datetime.strptime(t4a, "%Y-%m-%d %H:%M:%S")
ms = int(t4b.ljust(6, '0')[:6])
return t5.replace(microsecond=ms)
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/utils/utils.py#L267-L282
|
SiLab-Bonn/pyBAR
|
pybar/utils/utils.py
|
get_float_time
|
def get_float_time():
'''returns time as double precision floats - Time64 in pytables - mapping to and from python datetime's
'''
t1 = time.time()
t2 = datetime.datetime.fromtimestamp(t1)
return time.mktime(t2.timetuple()) + 1e-6 * t2.microsecond
|
python
|
def get_float_time():
'''returns time as double precision floats - Time64 in pytables - mapping to and from python datetime's
'''
t1 = time.time()
t2 = datetime.datetime.fromtimestamp(t1)
return time.mktime(t2.timetuple()) + 1e-6 * t2.microsecond
|
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returns time as double precision floats - Time64 in pytables - mapping to and from python datetime's
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/utils/utils.py#L285-L291
|
SiLab-Bonn/pyBAR
|
pybar/utils/utils.py
|
groupby_dict
|
def groupby_dict(dictionary, key):
''' Group dict of dicts by key.
'''
return dict((k, list(g)) for k, g in itertools.groupby(sorted(dictionary.keys(), key=lambda name: dictionary[name][key]), key=lambda name: dictionary[name][key]))
|
python
|
def groupby_dict(dictionary, key):
''' Group dict of dicts by key.
'''
return dict((k, list(g)) for k, g in itertools.groupby(sorted(dictionary.keys(), key=lambda name: dictionary[name][key]), key=lambda name: dictionary[name][key]))
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/utils/utils.py#L313-L316
|
SiLab-Bonn/pyBAR
|
pybar/utils/utils.py
|
zip_nofill
|
def zip_nofill(*iterables):
'''Zipping iterables without fillvalue.
Note: https://stackoverflow.com/questions/38054593/zip-longest-without-fillvalue
'''
return (tuple([entry for entry in iterable if entry is not None]) for iterable in itertools.izip_longest(*iterables, fillvalue=None))
|
python
|
def zip_nofill(*iterables):
'''Zipping iterables without fillvalue.
Note: https://stackoverflow.com/questions/38054593/zip-longest-without-fillvalue
'''
return (tuple([entry for entry in iterable if entry is not None]) for iterable in itertools.izip_longest(*iterables, fillvalue=None))
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/utils/utils.py#L334-L339
|
SiLab-Bonn/pyBAR
|
pybar/utils/utils.py
|
find_file_dir_up
|
def find_file_dir_up(filename, path=None, n=None):
'''Finding file in directory upwards.
'''
if path is None:
path = os.getcwd()
i = 0
while True:
current_path = path
for _ in range(i):
current_path = os.path.split(current_path)[0]
if os.path.isfile(os.path.join(current_path, filename)): # found file and return
return os.path.join(current_path, filename)
elif os.path.dirname(current_path) == current_path: # root of filesystem
return
elif n is not None and i == n:
return
else: # file not found
i += 1
continue
|
python
|
def find_file_dir_up(filename, path=None, n=None):
'''Finding file in directory upwards.
'''
if path is None:
path = os.getcwd()
i = 0
while True:
current_path = path
for _ in range(i):
current_path = os.path.split(current_path)[0]
if os.path.isfile(os.path.join(current_path, filename)): # found file and return
return os.path.join(current_path, filename)
elif os.path.dirname(current_path) == current_path: # root of filesystem
return
elif n is not None and i == n:
return
else: # file not found
i += 1
continue
|
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Finding file in directory upwards.
|
[
"Finding",
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"in",
"directory",
"upwards",
"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/utils/utils.py#L342-L360
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register.py
|
load_configuration_from_text_file
|
def load_configuration_from_text_file(register, configuration_file):
'''Loading configuration from text files to register object
Parameters
----------
register : pybar.fei4.register object
configuration_file : string
Full path (directory and filename) of the configuration file. If name is not given, reload configuration from file.
'''
logging.info("Loading configuration: %s" % configuration_file)
register.configuration_file = configuration_file
config_dict = parse_global_config(register.configuration_file)
if 'Flavor' in config_dict:
flavor = config_dict.pop('Flavor').lower()
if register.flavor:
pass
else:
register.init_fe_type(flavor)
else:
if register.flavor:
pass
else:
raise ValueError('Flavor not specified')
if 'Chip_ID' in config_dict:
chip_id = config_dict.pop('Chip_ID')
if register.chip_address:
pass
else:
register.set_chip_address(chip_address=chip_id & 0x7, broadcast=True if chip_id & 0x8 else False)
elif 'Chip_Address' in config_dict:
chip_address = config_dict.pop('Chip_Address')
if register.chip_address:
pass
else:
register.set_chip_address(chip_address)
else:
if register.chip_id_initialized:
pass
else:
raise ValueError('Chip address not specified')
global_registers_configured = []
pixel_registers_configured = []
for key in config_dict.keys():
value = config_dict.pop(key)
if key in register.global_registers:
register.set_global_register_value(key, value)
global_registers_configured.append(key)
elif key in register.pixel_registers:
register.set_pixel_register_value(key, value)
pixel_registers_configured.append(key)
elif key in register.calibration_parameters:
register.calibration_parameters[key] = value
else:
register.miscellaneous[key] = value
global_registers = register.get_global_register_attributes('name', readonly=False)
pixel_registers = register.pixel_registers.keys()
global_registers_not_configured = set(global_registers).difference(global_registers_configured)
pixel_registers_not_configured = set(pixel_registers).difference(pixel_registers_configured)
if global_registers_not_configured:
logging.warning("Following global register(s) not configured: {}".format(', '.join('\'' + reg + '\'' for reg in global_registers_not_configured)))
if pixel_registers_not_configured:
logging.warning("Following pixel register(s) not configured: {}".format(', '.join('\'' + reg + '\'' for reg in pixel_registers_not_configured)))
if register.miscellaneous:
logging.warning("Found following unknown parameter(s): {}".format(', '.join('\'' + parameter + '\'' for parameter in register.miscellaneous.iterkeys())))
|
python
|
def load_configuration_from_text_file(register, configuration_file):
'''Loading configuration from text files to register object
Parameters
----------
register : pybar.fei4.register object
configuration_file : string
Full path (directory and filename) of the configuration file. If name is not given, reload configuration from file.
'''
logging.info("Loading configuration: %s" % configuration_file)
register.configuration_file = configuration_file
config_dict = parse_global_config(register.configuration_file)
if 'Flavor' in config_dict:
flavor = config_dict.pop('Flavor').lower()
if register.flavor:
pass
else:
register.init_fe_type(flavor)
else:
if register.flavor:
pass
else:
raise ValueError('Flavor not specified')
if 'Chip_ID' in config_dict:
chip_id = config_dict.pop('Chip_ID')
if register.chip_address:
pass
else:
register.set_chip_address(chip_address=chip_id & 0x7, broadcast=True if chip_id & 0x8 else False)
elif 'Chip_Address' in config_dict:
chip_address = config_dict.pop('Chip_Address')
if register.chip_address:
pass
else:
register.set_chip_address(chip_address)
else:
if register.chip_id_initialized:
pass
else:
raise ValueError('Chip address not specified')
global_registers_configured = []
pixel_registers_configured = []
for key in config_dict.keys():
value = config_dict.pop(key)
if key in register.global_registers:
register.set_global_register_value(key, value)
global_registers_configured.append(key)
elif key in register.pixel_registers:
register.set_pixel_register_value(key, value)
pixel_registers_configured.append(key)
elif key in register.calibration_parameters:
register.calibration_parameters[key] = value
else:
register.miscellaneous[key] = value
global_registers = register.get_global_register_attributes('name', readonly=False)
pixel_registers = register.pixel_registers.keys()
global_registers_not_configured = set(global_registers).difference(global_registers_configured)
pixel_registers_not_configured = set(pixel_registers).difference(pixel_registers_configured)
if global_registers_not_configured:
logging.warning("Following global register(s) not configured: {}".format(', '.join('\'' + reg + '\'' for reg in global_registers_not_configured)))
if pixel_registers_not_configured:
logging.warning("Following pixel register(s) not configured: {}".format(', '.join('\'' + reg + '\'' for reg in pixel_registers_not_configured)))
if register.miscellaneous:
logging.warning("Found following unknown parameter(s): {}".format(', '.join('\'' + parameter + '\'' for parameter in register.miscellaneous.iterkeys())))
|
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Loading configuration from text files to register object
Parameters
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register : pybar.fei4.register object
configuration_file : string
Full path (directory and filename) of the configuration file. If name is not given, reload configuration from file.
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register.py#L715-L781
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register.py
|
load_configuration_from_hdf5
|
def load_configuration_from_hdf5(register, configuration_file, node=''):
'''Loading configuration from HDF5 file to register object
Parameters
----------
register : pybar.fei4.register object
configuration_file : string, file
Filename of the HDF5 configuration file or file object.
node : string
Additional identifier (subgroup). Useful when more than one configuration is stored inside a HDF5 file.
'''
def load_conf():
logging.info("Loading configuration: %s" % h5_file.filename)
register.configuration_file = h5_file.filename
if node:
configuration_group = h5_file.root.configuration.node
else:
configuration_group = h5_file.root.configuration
# miscellaneous
for row in configuration_group.miscellaneous:
name = row['name']
try:
value = literal_eval(row['value'])
except ValueError:
value = row['value']
if name == 'Flavor':
if register.flavor:
pass
else:
register.init_fe_type(value)
elif name == 'Chip_ID':
if register.chip_address:
pass
else:
register.set_chip_address(chip_address=value & 0x7, broadcast=True if value & 0x8 else False)
elif name == 'Chip_Address':
if register.chip_address:
pass
else:
register.set_chip_address(chip_address=value, broadcast=False)
else:
register.miscellaneous[name] = value
if register.flavor:
pass
else:
raise ValueError('Flavor not specified')
if register.chip_id_initialized:
pass
else:
raise ValueError('Chip address not specified')
# calibration parameters
for row in configuration_group.calibration_parameters:
name = row['name']
value = row['value']
register.calibration_parameters[name] = literal_eval(value)
# global
for row in configuration_group.global_register:
name = row['name']
value = row['value']
register.set_global_register_value(name, literal_eval(value))
# pixels
for pixel_reg in h5_file.iter_nodes(configuration_group, 'CArray'): # ['Enable', 'TDAC', 'C_High', 'C_Low', 'Imon', 'FDAC', 'EnableDigInj']:
if pixel_reg.name in register.pixel_registers:
register.set_pixel_register_value(pixel_reg.name, np.asarray(pixel_reg).T) # np.asarray(h5_file.get_node(configuration_group, name=pixel_reg)).T
if isinstance(configuration_file, tb.file.File):
h5_file = configuration_file
load_conf()
else:
with tb.open_file(configuration_file, mode="r", title='') as h5_file:
load_conf()
|
python
|
def load_configuration_from_hdf5(register, configuration_file, node=''):
'''Loading configuration from HDF5 file to register object
Parameters
----------
register : pybar.fei4.register object
configuration_file : string, file
Filename of the HDF5 configuration file or file object.
node : string
Additional identifier (subgroup). Useful when more than one configuration is stored inside a HDF5 file.
'''
def load_conf():
logging.info("Loading configuration: %s" % h5_file.filename)
register.configuration_file = h5_file.filename
if node:
configuration_group = h5_file.root.configuration.node
else:
configuration_group = h5_file.root.configuration
# miscellaneous
for row in configuration_group.miscellaneous:
name = row['name']
try:
value = literal_eval(row['value'])
except ValueError:
value = row['value']
if name == 'Flavor':
if register.flavor:
pass
else:
register.init_fe_type(value)
elif name == 'Chip_ID':
if register.chip_address:
pass
else:
register.set_chip_address(chip_address=value & 0x7, broadcast=True if value & 0x8 else False)
elif name == 'Chip_Address':
if register.chip_address:
pass
else:
register.set_chip_address(chip_address=value, broadcast=False)
else:
register.miscellaneous[name] = value
if register.flavor:
pass
else:
raise ValueError('Flavor not specified')
if register.chip_id_initialized:
pass
else:
raise ValueError('Chip address not specified')
# calibration parameters
for row in configuration_group.calibration_parameters:
name = row['name']
value = row['value']
register.calibration_parameters[name] = literal_eval(value)
# global
for row in configuration_group.global_register:
name = row['name']
value = row['value']
register.set_global_register_value(name, literal_eval(value))
# pixels
for pixel_reg in h5_file.iter_nodes(configuration_group, 'CArray'): # ['Enable', 'TDAC', 'C_High', 'C_Low', 'Imon', 'FDAC', 'EnableDigInj']:
if pixel_reg.name in register.pixel_registers:
register.set_pixel_register_value(pixel_reg.name, np.asarray(pixel_reg).T) # np.asarray(h5_file.get_node(configuration_group, name=pixel_reg)).T
if isinstance(configuration_file, tb.file.File):
h5_file = configuration_file
load_conf()
else:
with tb.open_file(configuration_file, mode="r", title='') as h5_file:
load_conf()
|
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register : pybar.fei4.register object
configuration_file : string, file
Filename of the HDF5 configuration file or file object.
node : string
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register.py#L784-L860
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register.py
|
save_configuration_to_text_file
|
def save_configuration_to_text_file(register, configuration_file):
'''Saving configuration to text files from register object
Parameters
----------
register : pybar.fei4.register object
configuration_file : string
Filename of the configuration file.
'''
configuration_path, filename = os.path.split(configuration_file)
if os.path.split(configuration_path)[1] == 'configs':
configuration_path = os.path.split(configuration_path)[0]
filename = os.path.splitext(filename)[0].strip()
register.configuration_file = os.path.join(os.path.join(configuration_path, 'configs'), filename + ".cfg")
if os.path.isfile(register.configuration_file):
logging.warning("Overwriting configuration: %s", register.configuration_file)
else:
logging.info("Saving configuration: %s" % register.configuration_file)
pixel_reg_dict = {}
for path in ["tdacs", "fdacs", "masks", "configs"]:
configuration_file_path = os.path.join(configuration_path, path)
if not os.path.exists(configuration_file_path):
os.makedirs(configuration_file_path)
if path == "tdacs":
dac = register.get_pixel_register_objects(name="TDAC")[0]
dac_config_path = os.path.join(configuration_file_path, "_".join([dac['name'].lower(), filename]) + ".dat")
write_pixel_dac_config(dac_config_path, dac['value'])
pixel_reg_dict[dac['name']] = os.path.relpath(dac_config_path, os.path.dirname(register.configuration_file))
elif path == "fdacs":
dac = register.get_pixel_register_objects(name="FDAC")[0]
dac_config_path = os.path.join(configuration_file_path, "_".join([dac['name'].lower(), filename]) + ".dat")
write_pixel_dac_config(dac_config_path, dac['value'])
pixel_reg_dict[dac['name']] = os.path.relpath(dac_config_path, os.path.dirname(register.configuration_file))
elif path == "masks":
masks = register.get_pixel_register_objects(bitlength=1)
for mask in masks:
dac_config_path = os.path.join(configuration_file_path, "_".join([mask['name'].lower(), filename]) + ".dat")
write_pixel_mask_config(dac_config_path, mask['value'])
pixel_reg_dict[mask['name']] = os.path.relpath(dac_config_path, os.path.dirname(register.configuration_file))
elif path == "configs":
with open(register.configuration_file, 'w') as f:
lines = []
lines.append("# FEI4 Flavor\n")
lines.append('%s %s\n' % ('Flavor', register.flavor))
lines.append("\n# FEI4 Chip ID\n")
lines.append('%s %d\n' % ('Chip_ID', register.chip_id))
lines.append("\n# FEI4 Global Registers\n")
global_regs = register.get_global_register_objects(readonly=False)
for global_reg in sorted(global_regs, key=itemgetter('name')):
lines.append('%s %d\n' % (global_reg['name'], global_reg['value']))
lines.append("\n# FEI4 Pixel Registers\n")
for key in sorted(pixel_reg_dict):
lines.append('%s %s\n' % (key, pixel_reg_dict[key]))
lines.append("\n# FEI4 Calibration Parameters\n")
for key in register.calibration_parameters:
if register.calibration_parameters[key] is None:
lines.append('%s %s\n' % (key, register.calibration_parameters[key]))
elif isinstance(register.calibration_parameters[key], (float, int, long)):
lines.append('%s %s\n' % (key, round(register.calibration_parameters[key], 4)))
elif isinstance(register.calibration_parameters[key], list):
lines.append('%s %s\n' % (key, [round(elem, 2) for elem in register.calibration_parameters[key]]))
else:
raise ValueError('type %s not supported' % type(register.calibration_parameters[key]))
if register.miscellaneous:
lines.append("\n# Miscellaneous\n")
for key, value in register.miscellaneous.iteritems():
lines.append('%s %s\n' % (key, value))
f.writelines(lines)
|
python
|
def save_configuration_to_text_file(register, configuration_file):
'''Saving configuration to text files from register object
Parameters
----------
register : pybar.fei4.register object
configuration_file : string
Filename of the configuration file.
'''
configuration_path, filename = os.path.split(configuration_file)
if os.path.split(configuration_path)[1] == 'configs':
configuration_path = os.path.split(configuration_path)[0]
filename = os.path.splitext(filename)[0].strip()
register.configuration_file = os.path.join(os.path.join(configuration_path, 'configs'), filename + ".cfg")
if os.path.isfile(register.configuration_file):
logging.warning("Overwriting configuration: %s", register.configuration_file)
else:
logging.info("Saving configuration: %s" % register.configuration_file)
pixel_reg_dict = {}
for path in ["tdacs", "fdacs", "masks", "configs"]:
configuration_file_path = os.path.join(configuration_path, path)
if not os.path.exists(configuration_file_path):
os.makedirs(configuration_file_path)
if path == "tdacs":
dac = register.get_pixel_register_objects(name="TDAC")[0]
dac_config_path = os.path.join(configuration_file_path, "_".join([dac['name'].lower(), filename]) + ".dat")
write_pixel_dac_config(dac_config_path, dac['value'])
pixel_reg_dict[dac['name']] = os.path.relpath(dac_config_path, os.path.dirname(register.configuration_file))
elif path == "fdacs":
dac = register.get_pixel_register_objects(name="FDAC")[0]
dac_config_path = os.path.join(configuration_file_path, "_".join([dac['name'].lower(), filename]) + ".dat")
write_pixel_dac_config(dac_config_path, dac['value'])
pixel_reg_dict[dac['name']] = os.path.relpath(dac_config_path, os.path.dirname(register.configuration_file))
elif path == "masks":
masks = register.get_pixel_register_objects(bitlength=1)
for mask in masks:
dac_config_path = os.path.join(configuration_file_path, "_".join([mask['name'].lower(), filename]) + ".dat")
write_pixel_mask_config(dac_config_path, mask['value'])
pixel_reg_dict[mask['name']] = os.path.relpath(dac_config_path, os.path.dirname(register.configuration_file))
elif path == "configs":
with open(register.configuration_file, 'w') as f:
lines = []
lines.append("# FEI4 Flavor\n")
lines.append('%s %s\n' % ('Flavor', register.flavor))
lines.append("\n# FEI4 Chip ID\n")
lines.append('%s %d\n' % ('Chip_ID', register.chip_id))
lines.append("\n# FEI4 Global Registers\n")
global_regs = register.get_global_register_objects(readonly=False)
for global_reg in sorted(global_regs, key=itemgetter('name')):
lines.append('%s %d\n' % (global_reg['name'], global_reg['value']))
lines.append("\n# FEI4 Pixel Registers\n")
for key in sorted(pixel_reg_dict):
lines.append('%s %s\n' % (key, pixel_reg_dict[key]))
lines.append("\n# FEI4 Calibration Parameters\n")
for key in register.calibration_parameters:
if register.calibration_parameters[key] is None:
lines.append('%s %s\n' % (key, register.calibration_parameters[key]))
elif isinstance(register.calibration_parameters[key], (float, int, long)):
lines.append('%s %s\n' % (key, round(register.calibration_parameters[key], 4)))
elif isinstance(register.calibration_parameters[key], list):
lines.append('%s %s\n' % (key, [round(elem, 2) for elem in register.calibration_parameters[key]]))
else:
raise ValueError('type %s not supported' % type(register.calibration_parameters[key]))
if register.miscellaneous:
lines.append("\n# Miscellaneous\n")
for key, value in register.miscellaneous.iteritems():
lines.append('%s %s\n' % (key, value))
f.writelines(lines)
|
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] |
Saving configuration to text files from register object
Parameters
----------
register : pybar.fei4.register object
configuration_file : string
Filename of the configuration file.
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register.py#L863-L930
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register.py
|
save_configuration_to_hdf5
|
def save_configuration_to_hdf5(register, configuration_file, name=''):
'''Saving configuration to HDF5 file from register object
Parameters
----------
register : pybar.fei4.register object
configuration_file : string, file
Filename of the HDF5 configuration file or file object.
name : string
Additional identifier (subgroup). Useful when storing more than one configuration inside a HDF5 file.
'''
def save_conf():
logging.info("Saving configuration: %s" % h5_file.filename)
register.configuration_file = h5_file.filename
try:
configuration_group = h5_file.create_group(h5_file.root, "configuration")
except tb.NodeError:
configuration_group = h5_file.root.configuration
if name:
try:
configuration_group = h5_file.create_group(configuration_group, name)
except tb.NodeError:
configuration_group = h5_file.root.configuration.name
# calibration_parameters
try:
h5_file.remove_node(configuration_group, name='calibration_parameters')
except tb.NodeError:
pass
calibration_data_table = h5_file.create_table(configuration_group, name='calibration_parameters', description=NameValue, title='calibration_parameters')
calibration_data_row = calibration_data_table.row
for key, value in register.calibration_parameters.iteritems():
calibration_data_row['name'] = key
calibration_data_row['value'] = str(value)
calibration_data_row.append()
calibration_data_table.flush()
# miscellaneous
try:
h5_file.remove_node(configuration_group, name='miscellaneous')
except tb.NodeError:
pass
miscellaneous_data_table = h5_file.create_table(configuration_group, name='miscellaneous', description=NameValue, title='miscellaneous')
miscellaneous_data_row = miscellaneous_data_table.row
miscellaneous_data_row['name'] = 'Flavor'
miscellaneous_data_row['value'] = register.flavor
miscellaneous_data_row.append()
miscellaneous_data_row['name'] = 'Chip_ID'
miscellaneous_data_row['value'] = register.chip_id
miscellaneous_data_row.append()
for key, value in register.miscellaneous.iteritems():
miscellaneous_data_row['name'] = key
miscellaneous_data_row['value'] = value
miscellaneous_data_row.append()
miscellaneous_data_table.flush()
# global
try:
h5_file.remove_node(configuration_group, name='global_register')
except tb.NodeError:
pass
global_data_table = h5_file.create_table(configuration_group, name='global_register', description=NameValue, title='global_register')
global_data_table_row = global_data_table.row
global_regs = register.get_global_register_objects(readonly=False)
for global_reg in sorted(global_regs, key=itemgetter('name')):
global_data_table_row['name'] = global_reg['name']
global_data_table_row['value'] = global_reg['value'] # TODO: some function that converts to bin, hex
global_data_table_row.append()
global_data_table.flush()
# pixel
for pixel_reg in register.pixel_registers.itervalues():
try:
h5_file.remove_node(configuration_group, name=pixel_reg['name'])
except tb.NodeError:
pass
data = pixel_reg['value'].T
atom = tb.Atom.from_dtype(data.dtype)
ds = h5_file.create_carray(configuration_group, name=pixel_reg['name'], atom=atom, shape=data.shape, title=pixel_reg['name'])
ds[:] = data
if isinstance(configuration_file, tb.file.File):
h5_file = configuration_file
save_conf()
else:
with tb.open_file(configuration_file, mode="a", title='') as h5_file:
save_conf()
|
python
|
def save_configuration_to_hdf5(register, configuration_file, name=''):
'''Saving configuration to HDF5 file from register object
Parameters
----------
register : pybar.fei4.register object
configuration_file : string, file
Filename of the HDF5 configuration file or file object.
name : string
Additional identifier (subgroup). Useful when storing more than one configuration inside a HDF5 file.
'''
def save_conf():
logging.info("Saving configuration: %s" % h5_file.filename)
register.configuration_file = h5_file.filename
try:
configuration_group = h5_file.create_group(h5_file.root, "configuration")
except tb.NodeError:
configuration_group = h5_file.root.configuration
if name:
try:
configuration_group = h5_file.create_group(configuration_group, name)
except tb.NodeError:
configuration_group = h5_file.root.configuration.name
# calibration_parameters
try:
h5_file.remove_node(configuration_group, name='calibration_parameters')
except tb.NodeError:
pass
calibration_data_table = h5_file.create_table(configuration_group, name='calibration_parameters', description=NameValue, title='calibration_parameters')
calibration_data_row = calibration_data_table.row
for key, value in register.calibration_parameters.iteritems():
calibration_data_row['name'] = key
calibration_data_row['value'] = str(value)
calibration_data_row.append()
calibration_data_table.flush()
# miscellaneous
try:
h5_file.remove_node(configuration_group, name='miscellaneous')
except tb.NodeError:
pass
miscellaneous_data_table = h5_file.create_table(configuration_group, name='miscellaneous', description=NameValue, title='miscellaneous')
miscellaneous_data_row = miscellaneous_data_table.row
miscellaneous_data_row['name'] = 'Flavor'
miscellaneous_data_row['value'] = register.flavor
miscellaneous_data_row.append()
miscellaneous_data_row['name'] = 'Chip_ID'
miscellaneous_data_row['value'] = register.chip_id
miscellaneous_data_row.append()
for key, value in register.miscellaneous.iteritems():
miscellaneous_data_row['name'] = key
miscellaneous_data_row['value'] = value
miscellaneous_data_row.append()
miscellaneous_data_table.flush()
# global
try:
h5_file.remove_node(configuration_group, name='global_register')
except tb.NodeError:
pass
global_data_table = h5_file.create_table(configuration_group, name='global_register', description=NameValue, title='global_register')
global_data_table_row = global_data_table.row
global_regs = register.get_global_register_objects(readonly=False)
for global_reg in sorted(global_regs, key=itemgetter('name')):
global_data_table_row['name'] = global_reg['name']
global_data_table_row['value'] = global_reg['value'] # TODO: some function that converts to bin, hex
global_data_table_row.append()
global_data_table.flush()
# pixel
for pixel_reg in register.pixel_registers.itervalues():
try:
h5_file.remove_node(configuration_group, name=pixel_reg['name'])
except tb.NodeError:
pass
data = pixel_reg['value'].T
atom = tb.Atom.from_dtype(data.dtype)
ds = h5_file.create_carray(configuration_group, name=pixel_reg['name'], atom=atom, shape=data.shape, title=pixel_reg['name'])
ds[:] = data
if isinstance(configuration_file, tb.file.File):
h5_file = configuration_file
save_conf()
else:
with tb.open_file(configuration_file, mode="a", title='') as h5_file:
save_conf()
|
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Saving configuration to HDF5 file from register object
Parameters
----------
register : pybar.fei4.register object
configuration_file : string, file
Filename of the HDF5 configuration file or file object.
name : string
Additional identifier (subgroup). Useful when storing more than one configuration inside a HDF5 file.
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register.py#L933-L1019
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register.py
|
FEI4Register.load_configuration
|
def load_configuration(self, configuration_file):
'''Loading configuration
Parameters
----------
configuration_file : string
Path to the configuration file (text or HDF5 file).
'''
if os.path.isfile(configuration_file):
if not isinstance(configuration_file, tb.file.File) and os.path.splitext(configuration_file)[1].strip().lower() != ".h5":
load_configuration_from_text_file(self, configuration_file)
else:
load_configuration_from_hdf5(self, configuration_file)
else:
raise ValueError('Cannot find configuration file specified: %s' % configuration_file)
|
python
|
def load_configuration(self, configuration_file):
'''Loading configuration
Parameters
----------
configuration_file : string
Path to the configuration file (text or HDF5 file).
'''
if os.path.isfile(configuration_file):
if not isinstance(configuration_file, tb.file.File) and os.path.splitext(configuration_file)[1].strip().lower() != ".h5":
load_configuration_from_text_file(self, configuration_file)
else:
load_configuration_from_hdf5(self, configuration_file)
else:
raise ValueError('Cannot find configuration file specified: %s' % configuration_file)
|
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"configuration_file",
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] |
Loading configuration
Parameters
----------
configuration_file : string
Path to the configuration file (text or HDF5 file).
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register.py#L135-L149
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register.py
|
FEI4Register.save_configuration
|
def save_configuration(self, configuration_file):
'''Saving configuration
Parameters
----------
configuration_file : string
Filename of the configuration file.
'''
if not isinstance(configuration_file, tb.file.File) and os.path.splitext(configuration_file)[1].strip().lower() != ".h5":
return save_configuration_to_text_file(self, configuration_file)
else:
return save_configuration_to_hdf5(self, configuration_file)
|
python
|
def save_configuration(self, configuration_file):
'''Saving configuration
Parameters
----------
configuration_file : string
Filename of the configuration file.
'''
if not isinstance(configuration_file, tb.file.File) and os.path.splitext(configuration_file)[1].strip().lower() != ".h5":
return save_configuration_to_text_file(self, configuration_file)
else:
return save_configuration_to_hdf5(self, configuration_file)
|
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Saving configuration
Parameters
----------
configuration_file : string
Filename of the configuration file.
|
[
"Saving",
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"configuration_file",
":",
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"Filename",
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"file",
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] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register.py#L151-L162
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register.py
|
FEI4Register.get_commands
|
def get_commands(self, command_name, **kwargs):
"""get fe_command from command name and keyword arguments
wrapper for build_commands()
implements FEI4 specific behavior
"""
chip_id = kwargs.pop("ChipID", self.chip_id_bitarray)
commands = []
if command_name == "zeros":
bv = bitarray(endian='little')
if "length" in kwargs:
bv += bitarray(kwargs["length"], endian='little') # initialized from int, bits may be random
elif kwargs:
raise ValueError("Unknown parameter(s): %s" % ", ".join(kwargs.iterkeys()))
bv.setall(0) # all bits to zero
commands.append(bv)
elif command_name == "ones":
bv = bitarray(endian='little')
if "length" in kwargs:
bv += bitarray(kwargs["length"], endian='little') # initialized from int, bits may be random
elif kwargs:
raise ValueError("Unknown parameter(s): %s" % ", ".join(kwargs.iterkeys()))
bv.setall(1) # all bits to one
commands.append(bv)
elif command_name == "WrRegister":
register_addresses = self.get_global_register_attributes("addresses", **kwargs)
register_bitsets = self.get_global_register_bitsets(register_addresses)
commands.extend([self.build_command(command_name, Address=register_address, GlobalData=register_bitset, ChipID=chip_id, **kwargs) for register_address, register_bitset in zip(register_addresses, register_bitsets)])
elif command_name == "RdRegister":
register_addresses = self.get_global_register_attributes('addresses', **kwargs)
commands.extend([self.build_command(command_name, Address=register_address, ChipID=chip_id) for register_address in register_addresses])
elif command_name == "WrFrontEnd":
registers = ["S0", "S1", "SR_Clr", "CalEn", "DIGHITIN_SEL", "GateHitOr", "ReadErrorReq", "StopClkPulse", "SR_Clock", "Efuse_Sense", "HITLD_IN", "Colpr_Mode", "Colpr_Addr"]
if self.fei4a:
registers.append("ReadSkipped")
elif self.fei4b:
registers.append("SR_Read")
self.create_restore_point()
dcs = kwargs.pop("dcs", range(40)) # set the double columns to latch
# in case of empty list
if not dcs:
dcs = range(40)
joint_write = kwargs.pop("joint_write", False)
same_mask_for_all_dc = kwargs.pop("same_mask_for_all_dc", False)
register_objects = self.get_pixel_register_objects(do_sort=['pxstrobe'], **kwargs)
# prepare for writing pixel registers
if not self.broadcast:
self.set_global_register_value("Colpr_Mode", 0) # write only to the addressed double-column
self.set_global_register_value("Colpr_Addr", 40) # ivalid address, grounded
# Broadcasting ConfMode not necessary, writing registers is also possible in RunMode
# commands.extend(self.get_commands("ConfMode", ChipID=8)) # set all chips to conf mode to receive commands#
# set all other chips to invalid addresses, to make broadcasting of WrRegister command possible
commands.extend(self.get_commands("WrRegister", name=["Colpr_Mode", "Colpr_Addr"], ChipID=8)) # braodcast
self.set_global_register_value("S0", 0)
self.set_global_register_value("S1", 0)
self.set_global_register_value("SR_Clr", 0)
self.set_global_register_value("CalEn", 0)
self.set_global_register_value("DIGHITIN_SEL", 0)
self.set_global_register_value("GateHitOr", 0)
self.set_global_register_value("ReadErrorReq", 0)
self.set_global_register_value("StopClkPulse", 0)
self.set_global_register_value("SR_Clock", 0)
self.set_global_register_value("Efuse_Sense", 0)
self.set_global_register_value("HITLD_IN", 0)
self.set_global_register_value("Colpr_Mode", 3 if same_mask_for_all_dc else 0) # write only the addressed double-column
self.set_global_register_value("Colpr_Addr", 0)
if self.fei4a:
self.set_global_register_value("ReadSkipped", 0)
elif self.fei4b:
self.set_global_register_value("SR_Read", 0)
commands.extend(self.get_commands("WrRegister", name=registers))
if joint_write:
pxstrobes = 0
first_read = True
do_latch = False
for register_object in register_objects:
if register_object['bitlength'] != 1:
raise ValueError('Pixel register %s: joint write not supported for pixel DACs' % register_object['name'])
pxstrobe = register_object['pxstrobe']
if not isinstance(pxstrobe, basestring):
do_latch = True
pxstrobes += 2 ** register_object['pxstrobe']
if first_read:
pixel_reg_value = register_object['value']
first_read = False
else:
if np.array_equal(pixel_reg_value, register_object['value']):
pixel_reg_value = register_object['value']
else:
raise ValueError('Pixel register %s: joint write not supported, pixel register values must be equal' % register_object['name'])
if do_latch:
self.set_global_register_value("Latch_En", 1)
else:
self.set_global_register_value("Latch_En", 0)
self.set_global_register_value("Pixel_Strobes", pxstrobes)
commands.extend(self.get_commands("WrRegister", name=["Pixel_Strobes", "Latch_En"]))
for dc_no in (dcs[:1] if same_mask_for_all_dc else dcs):
self.set_global_register_value("Colpr_Addr", dc_no)
commands.extend(self.get_commands("WrRegister", name=["Colpr_Addr"]))
register_bitset = self.get_pixel_register_bitset(register_objects[0], 0, dc_no)
commands.extend([self.build_command(command_name, PixelData=register_bitset, ChipID=8, **kwargs)]) # broadcast
if do_latch:
commands.extend(self.get_commands("GlobalPulse", Width=0))
else:
for register_object in register_objects:
pxstrobe = register_object['pxstrobe']
if isinstance(pxstrobe, basestring):
do_latch = False
self.set_global_register_value("Pixel_Strobes", 0) # no latch
self.set_global_register_value("Latch_En", 0)
commands.extend(self.get_commands("WrRegister", name=["Pixel_Strobes", "Latch_En"]))
else:
do_latch = True
self.set_global_register_value("Latch_En", 1)
commands.extend(self.get_commands("WrRegister", name=["Latch_En"]))
bitlength = register_object['bitlength']
for bit_no, pxstrobe_bit_no in (enumerate(range(bitlength)) if (register_object['littleendian'] is False) else enumerate(reversed(range(bitlength)))):
if do_latch:
self.set_global_register_value("Pixel_Strobes", 2 ** (pxstrobe + bit_no))
commands.extend(self.get_commands("WrRegister", name=["Pixel_Strobes"]))
for dc_no in (dcs[:1] if same_mask_for_all_dc else dcs):
self.set_global_register_value("Colpr_Addr", dc_no)
commands.extend(self.get_commands("WrRegister", name=["Colpr_Addr"]))
register_bitset = self.get_pixel_register_bitset(register_object, pxstrobe_bit_no, dc_no)
commands.extend([self.build_command(command_name, PixelData=register_bitset, ChipID=8, **kwargs)]) # broadcast
if do_latch:
commands.extend(self.get_commands("GlobalPulse", Width=0))
self.restore(pixel_register=False)
commands.extend(self.get_commands("WrRegister", name=registers))
elif command_name == "RdFrontEnd":
registers = ["Conf_AddrEnable", "S0", "S1", "SR_Clr", "CalEn", "DIGHITIN_SEL", "GateHitOr", "ReadErrorReq", "StopClkPulse", "SR_Clock", "Efuse_Sense", "HITLD_IN", "Colpr_Mode", "Colpr_Addr", "Pixel_Strobes", "Latch_En"]
if self.fei4a:
registers.append("ReadSkipped")
elif self.fei4b:
registers.append("SR_Read")
self.create_restore_point()
dcs = kwargs.pop("dcs", range(40)) # set the double columns to latch
# in case of empty list
if not dcs:
dcs = range(40)
register_objects = self.get_pixel_register_objects(**kwargs)
self.set_global_register_value('Conf_AddrEnable', 1)
self.set_global_register_value("S0", 0)
self.set_global_register_value("S1", 0)
self.set_global_register_value("SR_Clr", 0)
if self.fei4b:
self.set_global_register_value("SR_Read", 0)
self.set_global_register_value("CalEn", 0)
self.set_global_register_value("DIGHITIN_SEL", 0)
self.set_global_register_value("GateHitOr", 0)
if self.fei4a:
self.set_global_register_value("ReadSkipped", 0)
self.set_global_register_value("ReadErrorReq", 0)
self.set_global_register_value("StopClkPulse", 0)
self.set_global_register_value("SR_Clock", 0)
self.set_global_register_value("Efuse_Sense", 0)
self.set_global_register_value("HITLD_IN", 0)
self.set_global_register_value("Colpr_Mode", 0) # write only the addressed double-column
self.set_global_register_value("Colpr_Addr", 0)
self.set_global_register_value("Latch_En", 0)
self.set_global_register_value("Pixel_Strobes", 0)
commands.extend(self.get_commands("WrRegister", name=registers))
for index, register_object in enumerate(register_objects): # make sure that EnableDigInj is first read back, because it is not latched
if register_object['name'] == 'EnableDigInj':
register_objects[0], register_objects[index] = register_objects[index], register_objects[0]
break
for register_object in register_objects:
pxstrobe = register_object['pxstrobe']
bitlength = register_object['bitlength']
for pxstrobe_bit_no in range(bitlength):
logging.debug('Pixel Register %s Bit %d', register_object['name'], pxstrobe_bit_no)
do_latch = True
try:
self.set_global_register_value("Pixel_Strobes", 2 ** (pxstrobe + pxstrobe_bit_no))
except TypeError: # thrown for not latched digInjection
self.set_global_register_value("Pixel_Strobes", 0) # do not latch
do_latch = False
commands.extend(self.get_commands("WrRegister", name=["Pixel_Strobes"]))
for dc_no in dcs:
self.set_global_register_value("Colpr_Addr", dc_no)
commands.extend(self.get_commands("WrRegister", name=["Colpr_Addr"]))
if do_latch is True:
self.set_global_register_value("S0", 1)
self.set_global_register_value("S1", 1)
self.set_global_register_value("SR_Clock", 1)
commands.extend(self.get_commands("WrRegister", name=["S0", "S1", "SR_Clock"]))
commands.extend(self.get_commands("GlobalPulse", Width=0))
self.set_global_register_value("S0", 0)
self.set_global_register_value("S1", 0)
self.set_global_register_value("SR_Clock", 0)
commands.extend(self.get_commands("WrRegister", name=["S0", "S1", "SR_Clock"]))
register_bitset = self.get_pixel_register_bitset(register_object, pxstrobe_bit_no if (register_object['littleendian'] is False) else register_object['bitlength'] - pxstrobe_bit_no - 1, dc_no)
if self.fei4b:
self.set_global_register_value("SR_Read", 1)
commands.extend(self.get_commands("WrRegister", name=["SR_Read"]))
commands.extend([self.build_command("WrFrontEnd", PixelData=register_bitset, ChipID=chip_id)])
if self.fei4b:
self.set_global_register_value("SR_Read", 0)
commands.extend(self.get_commands("WrRegister", name=["SR_Read"]))
self.restore(pixel_register=False)
commands.extend(self.get_commands("WrRegister", name=registers))
else:
commands.append(self.build_command(command_name, ChipID=chip_id, **kwargs))
return commands
|
python
|
def get_commands(self, command_name, **kwargs):
"""get fe_command from command name and keyword arguments
wrapper for build_commands()
implements FEI4 specific behavior
"""
chip_id = kwargs.pop("ChipID", self.chip_id_bitarray)
commands = []
if command_name == "zeros":
bv = bitarray(endian='little')
if "length" in kwargs:
bv += bitarray(kwargs["length"], endian='little') # initialized from int, bits may be random
elif kwargs:
raise ValueError("Unknown parameter(s): %s" % ", ".join(kwargs.iterkeys()))
bv.setall(0) # all bits to zero
commands.append(bv)
elif command_name == "ones":
bv = bitarray(endian='little')
if "length" in kwargs:
bv += bitarray(kwargs["length"], endian='little') # initialized from int, bits may be random
elif kwargs:
raise ValueError("Unknown parameter(s): %s" % ", ".join(kwargs.iterkeys()))
bv.setall(1) # all bits to one
commands.append(bv)
elif command_name == "WrRegister":
register_addresses = self.get_global_register_attributes("addresses", **kwargs)
register_bitsets = self.get_global_register_bitsets(register_addresses)
commands.extend([self.build_command(command_name, Address=register_address, GlobalData=register_bitset, ChipID=chip_id, **kwargs) for register_address, register_bitset in zip(register_addresses, register_bitsets)])
elif command_name == "RdRegister":
register_addresses = self.get_global_register_attributes('addresses', **kwargs)
commands.extend([self.build_command(command_name, Address=register_address, ChipID=chip_id) for register_address in register_addresses])
elif command_name == "WrFrontEnd":
registers = ["S0", "S1", "SR_Clr", "CalEn", "DIGHITIN_SEL", "GateHitOr", "ReadErrorReq", "StopClkPulse", "SR_Clock", "Efuse_Sense", "HITLD_IN", "Colpr_Mode", "Colpr_Addr"]
if self.fei4a:
registers.append("ReadSkipped")
elif self.fei4b:
registers.append("SR_Read")
self.create_restore_point()
dcs = kwargs.pop("dcs", range(40)) # set the double columns to latch
# in case of empty list
if not dcs:
dcs = range(40)
joint_write = kwargs.pop("joint_write", False)
same_mask_for_all_dc = kwargs.pop("same_mask_for_all_dc", False)
register_objects = self.get_pixel_register_objects(do_sort=['pxstrobe'], **kwargs)
# prepare for writing pixel registers
if not self.broadcast:
self.set_global_register_value("Colpr_Mode", 0) # write only to the addressed double-column
self.set_global_register_value("Colpr_Addr", 40) # ivalid address, grounded
# Broadcasting ConfMode not necessary, writing registers is also possible in RunMode
# commands.extend(self.get_commands("ConfMode", ChipID=8)) # set all chips to conf mode to receive commands#
# set all other chips to invalid addresses, to make broadcasting of WrRegister command possible
commands.extend(self.get_commands("WrRegister", name=["Colpr_Mode", "Colpr_Addr"], ChipID=8)) # braodcast
self.set_global_register_value("S0", 0)
self.set_global_register_value("S1", 0)
self.set_global_register_value("SR_Clr", 0)
self.set_global_register_value("CalEn", 0)
self.set_global_register_value("DIGHITIN_SEL", 0)
self.set_global_register_value("GateHitOr", 0)
self.set_global_register_value("ReadErrorReq", 0)
self.set_global_register_value("StopClkPulse", 0)
self.set_global_register_value("SR_Clock", 0)
self.set_global_register_value("Efuse_Sense", 0)
self.set_global_register_value("HITLD_IN", 0)
self.set_global_register_value("Colpr_Mode", 3 if same_mask_for_all_dc else 0) # write only the addressed double-column
self.set_global_register_value("Colpr_Addr", 0)
if self.fei4a:
self.set_global_register_value("ReadSkipped", 0)
elif self.fei4b:
self.set_global_register_value("SR_Read", 0)
commands.extend(self.get_commands("WrRegister", name=registers))
if joint_write:
pxstrobes = 0
first_read = True
do_latch = False
for register_object in register_objects:
if register_object['bitlength'] != 1:
raise ValueError('Pixel register %s: joint write not supported for pixel DACs' % register_object['name'])
pxstrobe = register_object['pxstrobe']
if not isinstance(pxstrobe, basestring):
do_latch = True
pxstrobes += 2 ** register_object['pxstrobe']
if first_read:
pixel_reg_value = register_object['value']
first_read = False
else:
if np.array_equal(pixel_reg_value, register_object['value']):
pixel_reg_value = register_object['value']
else:
raise ValueError('Pixel register %s: joint write not supported, pixel register values must be equal' % register_object['name'])
if do_latch:
self.set_global_register_value("Latch_En", 1)
else:
self.set_global_register_value("Latch_En", 0)
self.set_global_register_value("Pixel_Strobes", pxstrobes)
commands.extend(self.get_commands("WrRegister", name=["Pixel_Strobes", "Latch_En"]))
for dc_no in (dcs[:1] if same_mask_for_all_dc else dcs):
self.set_global_register_value("Colpr_Addr", dc_no)
commands.extend(self.get_commands("WrRegister", name=["Colpr_Addr"]))
register_bitset = self.get_pixel_register_bitset(register_objects[0], 0, dc_no)
commands.extend([self.build_command(command_name, PixelData=register_bitset, ChipID=8, **kwargs)]) # broadcast
if do_latch:
commands.extend(self.get_commands("GlobalPulse", Width=0))
else:
for register_object in register_objects:
pxstrobe = register_object['pxstrobe']
if isinstance(pxstrobe, basestring):
do_latch = False
self.set_global_register_value("Pixel_Strobes", 0) # no latch
self.set_global_register_value("Latch_En", 0)
commands.extend(self.get_commands("WrRegister", name=["Pixel_Strobes", "Latch_En"]))
else:
do_latch = True
self.set_global_register_value("Latch_En", 1)
commands.extend(self.get_commands("WrRegister", name=["Latch_En"]))
bitlength = register_object['bitlength']
for bit_no, pxstrobe_bit_no in (enumerate(range(bitlength)) if (register_object['littleendian'] is False) else enumerate(reversed(range(bitlength)))):
if do_latch:
self.set_global_register_value("Pixel_Strobes", 2 ** (pxstrobe + bit_no))
commands.extend(self.get_commands("WrRegister", name=["Pixel_Strobes"]))
for dc_no in (dcs[:1] if same_mask_for_all_dc else dcs):
self.set_global_register_value("Colpr_Addr", dc_no)
commands.extend(self.get_commands("WrRegister", name=["Colpr_Addr"]))
register_bitset = self.get_pixel_register_bitset(register_object, pxstrobe_bit_no, dc_no)
commands.extend([self.build_command(command_name, PixelData=register_bitset, ChipID=8, **kwargs)]) # broadcast
if do_latch:
commands.extend(self.get_commands("GlobalPulse", Width=0))
self.restore(pixel_register=False)
commands.extend(self.get_commands("WrRegister", name=registers))
elif command_name == "RdFrontEnd":
registers = ["Conf_AddrEnable", "S0", "S1", "SR_Clr", "CalEn", "DIGHITIN_SEL", "GateHitOr", "ReadErrorReq", "StopClkPulse", "SR_Clock", "Efuse_Sense", "HITLD_IN", "Colpr_Mode", "Colpr_Addr", "Pixel_Strobes", "Latch_En"]
if self.fei4a:
registers.append("ReadSkipped")
elif self.fei4b:
registers.append("SR_Read")
self.create_restore_point()
dcs = kwargs.pop("dcs", range(40)) # set the double columns to latch
# in case of empty list
if not dcs:
dcs = range(40)
register_objects = self.get_pixel_register_objects(**kwargs)
self.set_global_register_value('Conf_AddrEnable', 1)
self.set_global_register_value("S0", 0)
self.set_global_register_value("S1", 0)
self.set_global_register_value("SR_Clr", 0)
if self.fei4b:
self.set_global_register_value("SR_Read", 0)
self.set_global_register_value("CalEn", 0)
self.set_global_register_value("DIGHITIN_SEL", 0)
self.set_global_register_value("GateHitOr", 0)
if self.fei4a:
self.set_global_register_value("ReadSkipped", 0)
self.set_global_register_value("ReadErrorReq", 0)
self.set_global_register_value("StopClkPulse", 0)
self.set_global_register_value("SR_Clock", 0)
self.set_global_register_value("Efuse_Sense", 0)
self.set_global_register_value("HITLD_IN", 0)
self.set_global_register_value("Colpr_Mode", 0) # write only the addressed double-column
self.set_global_register_value("Colpr_Addr", 0)
self.set_global_register_value("Latch_En", 0)
self.set_global_register_value("Pixel_Strobes", 0)
commands.extend(self.get_commands("WrRegister", name=registers))
for index, register_object in enumerate(register_objects): # make sure that EnableDigInj is first read back, because it is not latched
if register_object['name'] == 'EnableDigInj':
register_objects[0], register_objects[index] = register_objects[index], register_objects[0]
break
for register_object in register_objects:
pxstrobe = register_object['pxstrobe']
bitlength = register_object['bitlength']
for pxstrobe_bit_no in range(bitlength):
logging.debug('Pixel Register %s Bit %d', register_object['name'], pxstrobe_bit_no)
do_latch = True
try:
self.set_global_register_value("Pixel_Strobes", 2 ** (pxstrobe + pxstrobe_bit_no))
except TypeError: # thrown for not latched digInjection
self.set_global_register_value("Pixel_Strobes", 0) # do not latch
do_latch = False
commands.extend(self.get_commands("WrRegister", name=["Pixel_Strobes"]))
for dc_no in dcs:
self.set_global_register_value("Colpr_Addr", dc_no)
commands.extend(self.get_commands("WrRegister", name=["Colpr_Addr"]))
if do_latch is True:
self.set_global_register_value("S0", 1)
self.set_global_register_value("S1", 1)
self.set_global_register_value("SR_Clock", 1)
commands.extend(self.get_commands("WrRegister", name=["S0", "S1", "SR_Clock"]))
commands.extend(self.get_commands("GlobalPulse", Width=0))
self.set_global_register_value("S0", 0)
self.set_global_register_value("S1", 0)
self.set_global_register_value("SR_Clock", 0)
commands.extend(self.get_commands("WrRegister", name=["S0", "S1", "SR_Clock"]))
register_bitset = self.get_pixel_register_bitset(register_object, pxstrobe_bit_no if (register_object['littleendian'] is False) else register_object['bitlength'] - pxstrobe_bit_no - 1, dc_no)
if self.fei4b:
self.set_global_register_value("SR_Read", 1)
commands.extend(self.get_commands("WrRegister", name=["SR_Read"]))
commands.extend([self.build_command("WrFrontEnd", PixelData=register_bitset, ChipID=chip_id)])
if self.fei4b:
self.set_global_register_value("SR_Read", 0)
commands.extend(self.get_commands("WrRegister", name=["SR_Read"]))
self.restore(pixel_register=False)
commands.extend(self.get_commands("WrRegister", name=registers))
else:
commands.append(self.build_command(command_name, ChipID=chip_id, **kwargs))
return commands
|
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] |
get fe_command from command name and keyword arguments
wrapper for build_commands()
implements FEI4 specific behavior
|
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"fe_command",
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"and",
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"build_commands",
"()",
"implements",
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"specific",
"behavior"
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register.py#L205-L410
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register.py
|
FEI4Register.build_command
|
def build_command(self, command_name, **kwargs):
"""build command from command_name and keyword values
Returns
-------
command_bitvector : list
List of bitarrays.
Usage
-----
Receives: command name as defined inside xml file, key-value-pairs as defined inside bit stream filed for each command
"""
# command_name = command_name.lower()
command_bitvector = bitarray(0, endian='little')
if command_name not in self.commands:
raise ValueError('Unknown command %s' % command_name)
command_object = self.commands[command_name]
command_parts = re.split(r'\s*[+]\s*', command_object['bitstream'])
# for index, part in enumerate(command_parts, start = 1): # loop over command parts
for part in command_parts: # loop over command parts
try:
command_part_object = self.commands[part]
except KeyError:
command_part_object = None
if command_part_object and 'bitstream'in command_part_object: # command parts of defined content and length, e.g. Slow, ...
if string_is_binary(command_part_object['bitstream']):
command_bitvector += bitarray(command_part_object['bitstream'], endian='little')
else:
command_bitvector += self.build_command(part, **kwargs)
elif command_part_object: # Command parts with any content of defined length, e.g. ChipID, Address, ...
if part in kwargs:
value = kwargs[part]
else:
raise ValueError('Value of command part %s not given' % part)
try:
command_bitvector += value
except TypeError: # value is no bitarray
if string_is_binary(value):
value = int(value, 2)
try:
command_bitvector += bitarray_from_value(value=int(value), size=command_part_object['bitlength'], fmt='I')
except Exception:
raise TypeError("Type of value not supported")
elif string_is_binary(part):
command_bitvector += bitarray(part, endian='little')
# elif part in kwargs.keys():
# command_bitvector += kwargs[command_name]
else:
raise ValueError("Cannot process command part %s" % part)
if command_bitvector.length() != command_object['bitlength']:
raise ValueError("Command has unexpected length")
if command_bitvector.length() == 0:
raise ValueError("Command has length 0")
return command_bitvector
|
python
|
def build_command(self, command_name, **kwargs):
"""build command from command_name and keyword values
Returns
-------
command_bitvector : list
List of bitarrays.
Usage
-----
Receives: command name as defined inside xml file, key-value-pairs as defined inside bit stream filed for each command
"""
# command_name = command_name.lower()
command_bitvector = bitarray(0, endian='little')
if command_name not in self.commands:
raise ValueError('Unknown command %s' % command_name)
command_object = self.commands[command_name]
command_parts = re.split(r'\s*[+]\s*', command_object['bitstream'])
# for index, part in enumerate(command_parts, start = 1): # loop over command parts
for part in command_parts: # loop over command parts
try:
command_part_object = self.commands[part]
except KeyError:
command_part_object = None
if command_part_object and 'bitstream'in command_part_object: # command parts of defined content and length, e.g. Slow, ...
if string_is_binary(command_part_object['bitstream']):
command_bitvector += bitarray(command_part_object['bitstream'], endian='little')
else:
command_bitvector += self.build_command(part, **kwargs)
elif command_part_object: # Command parts with any content of defined length, e.g. ChipID, Address, ...
if part in kwargs:
value = kwargs[part]
else:
raise ValueError('Value of command part %s not given' % part)
try:
command_bitvector += value
except TypeError: # value is no bitarray
if string_is_binary(value):
value = int(value, 2)
try:
command_bitvector += bitarray_from_value(value=int(value), size=command_part_object['bitlength'], fmt='I')
except Exception:
raise TypeError("Type of value not supported")
elif string_is_binary(part):
command_bitvector += bitarray(part, endian='little')
# elif part in kwargs.keys():
# command_bitvector += kwargs[command_name]
else:
raise ValueError("Cannot process command part %s" % part)
if command_bitvector.length() != command_object['bitlength']:
raise ValueError("Command has unexpected length")
if command_bitvector.length() == 0:
raise ValueError("Command has length 0")
return command_bitvector
|
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build command from command_name and keyword values
Returns
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command_bitvector : list
List of bitarrays.
Usage
-----
Receives: command name as defined inside xml file, key-value-pairs as defined inside bit stream filed for each command
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register.py#L412-L465
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register.py
|
FEI4Register.get_global_register_attributes
|
def get_global_register_attributes(self, register_attribute, do_sort=True, **kwargs):
"""Calculating register numbers from register names.
Usage: get_global_register_attributes("attribute_name", name = [regname_1, regname_2, ...], addresses = 2)
Receives: attribute name to be returned, dictionaries (kwargs) of register attributes and values for making cuts
Returns: list of attribute values that matches dictionaries of attributes
"""
# speed up of the most often used keyword name
try:
names = iterable(kwargs.pop('name'))
except KeyError:
register_attribute_list = []
else:
register_attribute_list = [self.global_registers[reg][register_attribute] for reg in names]
for keyword in kwargs.keys():
allowed_values = iterable(kwargs[keyword])
try:
register_attribute_list.extend(map(itemgetter(register_attribute), filter(lambda global_register: set(iterable(global_register[keyword])).intersection(allowed_values), self.global_registers.itervalues())))
except AttributeError:
pass
if not register_attribute_list and filter(None, kwargs.itervalues()):
raise ValueError('Global register attribute %s empty' % register_attribute)
if do_sort:
return sorted(set(flatten_iterable(register_attribute_list)))
else:
return flatten_iterable(register_attribute_list)
|
python
|
def get_global_register_attributes(self, register_attribute, do_sort=True, **kwargs):
"""Calculating register numbers from register names.
Usage: get_global_register_attributes("attribute_name", name = [regname_1, regname_2, ...], addresses = 2)
Receives: attribute name to be returned, dictionaries (kwargs) of register attributes and values for making cuts
Returns: list of attribute values that matches dictionaries of attributes
"""
# speed up of the most often used keyword name
try:
names = iterable(kwargs.pop('name'))
except KeyError:
register_attribute_list = []
else:
register_attribute_list = [self.global_registers[reg][register_attribute] for reg in names]
for keyword in kwargs.keys():
allowed_values = iterable(kwargs[keyword])
try:
register_attribute_list.extend(map(itemgetter(register_attribute), filter(lambda global_register: set(iterable(global_register[keyword])).intersection(allowed_values), self.global_registers.itervalues())))
except AttributeError:
pass
if not register_attribute_list and filter(None, kwargs.itervalues()):
raise ValueError('Global register attribute %s empty' % register_attribute)
if do_sort:
return sorted(set(flatten_iterable(register_attribute_list)))
else:
return flatten_iterable(register_attribute_list)
|
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Calculating register numbers from register names.
Usage: get_global_register_attributes("attribute_name", name = [regname_1, regname_2, ...], addresses = 2)
Receives: attribute name to be returned, dictionaries (kwargs) of register attributes and values for making cuts
Returns: list of attribute values that matches dictionaries of attributes
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register.py#L467-L493
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register.py
|
FEI4Register.get_global_register_objects
|
def get_global_register_objects(self, do_sort=None, reverse=False, **kwargs):
"""Generate register objects (list) from register name list
Usage: get_global_register_objects(name = ["Amp2Vbn", "GateHitOr", "DisableColumnCnfg"], address = [2, 3])
Receives: keyword lists of register names, addresses,... for making cuts
Returns: list of register objects
"""
# speed up of the most often used keyword name
try:
names = iterable(kwargs.pop('name'))
except KeyError:
register_objects = []
else:
register_objects = [self.global_registers[reg] for reg in names]
for keyword in kwargs.iterkeys():
allowed_values = iterable(kwargs[keyword])
register_objects.extend(filter(lambda global_register: set(iterable(global_register[keyword])).intersection(allowed_values), self.global_registers.itervalues()))
if not register_objects and filter(None, kwargs.itervalues()):
raise ValueError('Global register objects empty')
if do_sort:
return sorted(register_objects, key=itemgetter(*do_sort), reverse=reverse)
else:
return register_objects
|
python
|
def get_global_register_objects(self, do_sort=None, reverse=False, **kwargs):
"""Generate register objects (list) from register name list
Usage: get_global_register_objects(name = ["Amp2Vbn", "GateHitOr", "DisableColumnCnfg"], address = [2, 3])
Receives: keyword lists of register names, addresses,... for making cuts
Returns: list of register objects
"""
# speed up of the most often used keyword name
try:
names = iterable(kwargs.pop('name'))
except KeyError:
register_objects = []
else:
register_objects = [self.global_registers[reg] for reg in names]
for keyword in kwargs.iterkeys():
allowed_values = iterable(kwargs[keyword])
register_objects.extend(filter(lambda global_register: set(iterable(global_register[keyword])).intersection(allowed_values), self.global_registers.itervalues()))
if not register_objects and filter(None, kwargs.itervalues()):
raise ValueError('Global register objects empty')
if do_sort:
return sorted(register_objects, key=itemgetter(*do_sort), reverse=reverse)
else:
return register_objects
|
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Receives: keyword lists of register names, addresses,... for making cuts
Returns: list of register objects
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register.py#L495-L518
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register.py
|
FEI4Register.get_global_register_bitsets
|
def get_global_register_bitsets(self, register_addresses): # TOTO: add sorting
"""Calculating register bitsets from register addresses.
Usage: get_global_register_bitsets([regaddress_1, regaddress_2, ...])
Receives: list of register addresses
Returns: list of register bitsets
"""
register_bitsets = []
for register_address in register_addresses:
register_objects = self.get_global_register_objects(addresses=register_address)
register_bitset = bitarray(16, endian='little') # TODO remove hardcoded register size, see also below
register_bitset.setall(0)
register_littleendian = False
for register_object in register_objects:
if register_object['register_littleendian']: # check for register endianness
register_littleendian = True
if (16 * register_object['address'] + register_object['offset'] < 16 * (register_address + 1) and 16 * register_object['address'] + register_object['offset'] + register_object['bitlength'] > 16 * register_address):
reg = bitarray_from_value(value=register_object['value'], size=register_object['bitlength'])
if register_object['littleendian']:
reg.reverse()
# register_bitset[max(0, 16 * (register_object['address'] - register_address) + register_object['offset']):min(16, 16 * (register_object['address'] - register_address) + register_object['offset'] + register_object['bitlength'])] |= reg[max(0, 16 * (register_address - register_object['address']) - register_object['offset']):min(register_object['bitlength'], 16 * (register_address - register_object['address'] + 1) - register_object['offset'])] # [ bit(n) bit(n-1)... bit(0) ]
register_bitset[max(0, 16 - 16 * (register_object['address'] - register_address) - register_object['offset'] - register_object['bitlength']):min(16, 16 - 16 * (register_object['address'] - register_address) - register_object['offset'])] |= reg[max(0, register_object['bitlength'] - 16 - 16 * (register_address - register_object['address']) + register_object['offset']):min(register_object['bitlength'], register_object['bitlength'] + 16 - 16 * (register_address - register_object['address'] + 1) + register_object['offset'])] # [ bit(0)... bit(n-1) bit(n) ]
else:
raise Exception("wrong register object")
if register_littleendian:
register_bitset.reverse()
register_bitsets.append(register_bitset)
return register_bitsets
|
python
|
def get_global_register_bitsets(self, register_addresses): # TOTO: add sorting
"""Calculating register bitsets from register addresses.
Usage: get_global_register_bitsets([regaddress_1, regaddress_2, ...])
Receives: list of register addresses
Returns: list of register bitsets
"""
register_bitsets = []
for register_address in register_addresses:
register_objects = self.get_global_register_objects(addresses=register_address)
register_bitset = bitarray(16, endian='little') # TODO remove hardcoded register size, see also below
register_bitset.setall(0)
register_littleendian = False
for register_object in register_objects:
if register_object['register_littleendian']: # check for register endianness
register_littleendian = True
if (16 * register_object['address'] + register_object['offset'] < 16 * (register_address + 1) and 16 * register_object['address'] + register_object['offset'] + register_object['bitlength'] > 16 * register_address):
reg = bitarray_from_value(value=register_object['value'], size=register_object['bitlength'])
if register_object['littleendian']:
reg.reverse()
# register_bitset[max(0, 16 * (register_object['address'] - register_address) + register_object['offset']):min(16, 16 * (register_object['address'] - register_address) + register_object['offset'] + register_object['bitlength'])] |= reg[max(0, 16 * (register_address - register_object['address']) - register_object['offset']):min(register_object['bitlength'], 16 * (register_address - register_object['address'] + 1) - register_object['offset'])] # [ bit(n) bit(n-1)... bit(0) ]
register_bitset[max(0, 16 - 16 * (register_object['address'] - register_address) - register_object['offset'] - register_object['bitlength']):min(16, 16 - 16 * (register_object['address'] - register_address) - register_object['offset'])] |= reg[max(0, register_object['bitlength'] - 16 - 16 * (register_address - register_object['address']) + register_object['offset']):min(register_object['bitlength'], register_object['bitlength'] + 16 - 16 * (register_address - register_object['address'] + 1) + register_object['offset'])] # [ bit(0)... bit(n-1) bit(n) ]
else:
raise Exception("wrong register object")
if register_littleendian:
register_bitset.reverse()
register_bitsets.append(register_bitset)
return register_bitsets
|
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|
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train
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https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register.py#L520-L548
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register.py
|
FEI4Register.get_pixel_register_objects
|
def get_pixel_register_objects(self, do_sort=None, reverse=False, **kwargs):
"""Generate register objects (list) from register name list
Usage: get_pixel_register_objects(name = ["TDAC", "FDAC"])
Receives: keyword lists of register names, addresses,...
Returns: list of register objects
"""
# speed up of the most often used keyword name
try:
names = iterable(kwargs.pop('name'))
except KeyError:
register_objects = []
else:
register_objects = [self.pixel_registers[reg] for reg in names]
for keyword in kwargs.iterkeys():
allowed_values = iterable(kwargs[keyword])
register_objects.extend(filter(lambda pixel_register: pixel_register[keyword] in allowed_values, self.pixel_registers.itervalues()))
if not register_objects and filter(None, kwargs.itervalues()):
raise ValueError('Pixel register objects empty')
if do_sort:
return sorted(register_objects, key=itemgetter(*do_sort), reverse=reverse)
else:
return register_objects
|
python
|
def get_pixel_register_objects(self, do_sort=None, reverse=False, **kwargs):
"""Generate register objects (list) from register name list
Usage: get_pixel_register_objects(name = ["TDAC", "FDAC"])
Receives: keyword lists of register names, addresses,...
Returns: list of register objects
"""
# speed up of the most often used keyword name
try:
names = iterable(kwargs.pop('name'))
except KeyError:
register_objects = []
else:
register_objects = [self.pixel_registers[reg] for reg in names]
for keyword in kwargs.iterkeys():
allowed_values = iterable(kwargs[keyword])
register_objects.extend(filter(lambda pixel_register: pixel_register[keyword] in allowed_values, self.pixel_registers.itervalues()))
if not register_objects and filter(None, kwargs.itervalues()):
raise ValueError('Pixel register objects empty')
if do_sort:
return sorted(register_objects, key=itemgetter(*do_sort), reverse=reverse)
else:
return register_objects
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register.py#L550-L573
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register.py
|
FEI4Register.get_pixel_register_bitset
|
def get_pixel_register_bitset(self, register_object, bit_no, dc_no):
"""Calculating pixel register bitsets from pixel register addresses.
Usage: get_pixel_register_bitset(object, bit_number, double_column_number)
Receives: register object, bit number, double column number
Returns: double column bitset
"""
if not 0 <= dc_no < 40:
raise ValueError("Pixel register %s: DC out of range" % register_object['name'])
if not 0 <= bit_no < register_object['bitlength']:
raise ValueError("Pixel register %s: bit number out of range" % register_object['name'])
col0 = register_object['value'][dc_no * 2, :]
sel0 = (2 ** bit_no == (col0 & 2 ** bit_no))
bv0 = bitarray(sel0.tolist(), endian='little')
col1 = register_object['value'][dc_no * 2 + 1, :]
sel1 = (2 ** bit_no == (col1 & 2 ** bit_no))
# sel1 = sel1.astype(numpy.uint8) # copy of array
# sel1 = sel1.view(dtype=np.uint8) # in-place type conversion
bv1 = bitarray(sel1.tolist(), endian='little')
bv1.reverse() # shifted first
# bv = bv1+bv0
return bv1 + bv0
|
python
|
def get_pixel_register_bitset(self, register_object, bit_no, dc_no):
"""Calculating pixel register bitsets from pixel register addresses.
Usage: get_pixel_register_bitset(object, bit_number, double_column_number)
Receives: register object, bit number, double column number
Returns: double column bitset
"""
if not 0 <= dc_no < 40:
raise ValueError("Pixel register %s: DC out of range" % register_object['name'])
if not 0 <= bit_no < register_object['bitlength']:
raise ValueError("Pixel register %s: bit number out of range" % register_object['name'])
col0 = register_object['value'][dc_no * 2, :]
sel0 = (2 ** bit_no == (col0 & 2 ** bit_no))
bv0 = bitarray(sel0.tolist(), endian='little')
col1 = register_object['value'][dc_no * 2 + 1, :]
sel1 = (2 ** bit_no == (col1 & 2 ** bit_no))
# sel1 = sel1.astype(numpy.uint8) # copy of array
# sel1 = sel1.view(dtype=np.uint8) # in-place type conversion
bv1 = bitarray(sel1.tolist(), endian='little')
bv1.reverse() # shifted first
# bv = bv1+bv0
return bv1 + bv0
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register.py#L575-L597
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register.py
|
FEI4Register.create_restore_point
|
def create_restore_point(self, name=None):
'''Creating a configuration restore point.
Parameters
----------
name : str
Name of the restore point. If not given, a md5 hash will be generated.
'''
if name is None:
for i in iter(int, 1):
name = datetime.datetime.utcnow().strftime('%Y%m%d%H%M%S%f') + '_' + str(i)
try:
self.config_state[name]
except KeyError:
break
else:
pass
if name in self.config_state:
raise ValueError('Restore point %s already exists' % name)
self.config_state[name] = (copy.deepcopy(self.global_registers), copy.deepcopy(self.pixel_registers))
return name
|
python
|
def create_restore_point(self, name=None):
'''Creating a configuration restore point.
Parameters
----------
name : str
Name of the restore point. If not given, a md5 hash will be generated.
'''
if name is None:
for i in iter(int, 1):
name = datetime.datetime.utcnow().strftime('%Y%m%d%H%M%S%f') + '_' + str(i)
try:
self.config_state[name]
except KeyError:
break
else:
pass
if name in self.config_state:
raise ValueError('Restore point %s already exists' % name)
self.config_state[name] = (copy.deepcopy(self.global_registers), copy.deepcopy(self.pixel_registers))
return name
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register.py#L607-L627
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register.py
|
FEI4Register.restore
|
def restore(self, name=None, keep=False, last=True, global_register=True, pixel_register=True):
'''Restoring a configuration restore point.
Parameters
----------
name : str
Name of the restore point. If not given, a md5 hash will be generated.
keep : bool
Keeping restore point for later use.
last : bool
If name is not given, the latest restore point will be taken.
global_register : bool
Restore global register.
pixel_register : bool
Restore pixel register.
'''
if name is None:
if keep:
name = next(reversed(self.config_state)) if last else next(iter(self.config_state))
value = self.config_state[name]
else:
name, value = self.config_state.popitem(last=last)
else:
value = self.config_state[name]
if not keep:
value = copy.deepcopy(value) # make a copy before deleting object
del self.config_state[name]
if global_register:
self.global_registers = copy.deepcopy(value[0])
if pixel_register:
self.pixel_registers = copy.deepcopy(value[1])
|
python
|
def restore(self, name=None, keep=False, last=True, global_register=True, pixel_register=True):
'''Restoring a configuration restore point.
Parameters
----------
name : str
Name of the restore point. If not given, a md5 hash will be generated.
keep : bool
Keeping restore point for later use.
last : bool
If name is not given, the latest restore point will be taken.
global_register : bool
Restore global register.
pixel_register : bool
Restore pixel register.
'''
if name is None:
if keep:
name = next(reversed(self.config_state)) if last else next(iter(self.config_state))
value = self.config_state[name]
else:
name, value = self.config_state.popitem(last=last)
else:
value = self.config_state[name]
if not keep:
value = copy.deepcopy(value) # make a copy before deleting object
del self.config_state[name]
if global_register:
self.global_registers = copy.deepcopy(value[0])
if pixel_register:
self.pixel_registers = copy.deepcopy(value[1])
|
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Keeping restore point for later use.
last : bool
If name is not given, the latest restore point will be taken.
global_register : bool
Restore global register.
pixel_register : bool
Restore pixel register.
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register.py#L629-L660
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register.py
|
FEI4Register.clear_restore_points
|
def clear_restore_points(self, name=None):
'''Deleting all/a configuration restore points/point.
Parameters
----------
name : str
Name of the restore point to be deleted. If not given, all restore points will be deleted.
'''
if name is None:
self.config_state.clear()
else:
del self.config_state[name]
|
python
|
def clear_restore_points(self, name=None):
'''Deleting all/a configuration restore points/point.
Parameters
----------
name : str
Name of the restore point to be deleted. If not given, all restore points will be deleted.
'''
if name is None:
self.config_state.clear()
else:
del self.config_state[name]
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register.py#L662-L673
|
SiLab-Bonn/pyBAR
|
pybar/daq/readout_utils.py
|
save_configuration_dict
|
def save_configuration_dict(h5_file, configuation_name, configuration, **kwargs):
'''Stores any configuration dictionary to HDF5 file.
Parameters
----------
h5_file : string, file
Filename of the HDF5 configuration file or file object.
configuation_name : str
Configuration name. Will be used for table name.
configuration : dict
Configuration dictionary.
'''
def save_conf():
try:
h5_file.remove_node(h5_file.root.configuration, name=configuation_name)
except tb.NodeError:
pass
try:
configuration_group = h5_file.create_group(h5_file.root, "configuration")
except tb.NodeError:
configuration_group = h5_file.root.configuration
scan_param_table = h5_file.create_table(configuration_group, name=configuation_name, description=NameValue, title=configuation_name)
row_scan_param = scan_param_table.row
for key, value in dict.iteritems(configuration):
row_scan_param['name'] = key
row_scan_param['value'] = str(value)
row_scan_param.append()
scan_param_table.flush()
if isinstance(h5_file, tb.file.File):
save_conf()
else:
if os.path.splitext(h5_file)[1].strip().lower() != ".h5":
h5_file = os.path.splitext(h5_file)[0] + ".h5"
with tb.open_file(h5_file, mode="a", title='', **kwargs) as h5_file:
save_conf()
|
python
|
def save_configuration_dict(h5_file, configuation_name, configuration, **kwargs):
'''Stores any configuration dictionary to HDF5 file.
Parameters
----------
h5_file : string, file
Filename of the HDF5 configuration file or file object.
configuation_name : str
Configuration name. Will be used for table name.
configuration : dict
Configuration dictionary.
'''
def save_conf():
try:
h5_file.remove_node(h5_file.root.configuration, name=configuation_name)
except tb.NodeError:
pass
try:
configuration_group = h5_file.create_group(h5_file.root, "configuration")
except tb.NodeError:
configuration_group = h5_file.root.configuration
scan_param_table = h5_file.create_table(configuration_group, name=configuation_name, description=NameValue, title=configuation_name)
row_scan_param = scan_param_table.row
for key, value in dict.iteritems(configuration):
row_scan_param['name'] = key
row_scan_param['value'] = str(value)
row_scan_param.append()
scan_param_table.flush()
if isinstance(h5_file, tb.file.File):
save_conf()
else:
if os.path.splitext(h5_file)[1].strip().lower() != ".h5":
h5_file = os.path.splitext(h5_file)[0] + ".h5"
with tb.open_file(h5_file, mode="a", title='', **kwargs) as h5_file:
save_conf()
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configuation_name : str
Configuration name. Will be used for table name.
configuration : dict
Configuration dictionary.
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/daq/readout_utils.py#L13-L49
|
SiLab-Bonn/pyBAR
|
pybar/daq/readout_utils.py
|
convert_data_array
|
def convert_data_array(array, filter_func=None, converter_func=None): # TODO: add copy parameter, otherwise in-place
'''Filter and convert raw data numpy array (numpy.ndarray).
Parameters
----------
array : numpy.array
Raw data array.
filter_func : function
Function that takes array and returns true or false for each item in array.
converter_func : function
Function that takes array and returns an array or tuple of arrays.
Returns
-------
data_array : numpy.array
Data numpy array of specified dimension (converter_func) and content (filter_func)
'''
# if filter_func != None:
# if not hasattr(filter_func, '__call__'):
# raise ValueError('Filter is not callable')
if filter_func:
array = array[filter_func(array)]
# if converter_func != None:
# if not hasattr(converter_func, '__call__'):
# raise ValueError('Converter is not callable')
if converter_func:
array = converter_func(array)
return array
|
python
|
def convert_data_array(array, filter_func=None, converter_func=None): # TODO: add copy parameter, otherwise in-place
'''Filter and convert raw data numpy array (numpy.ndarray).
Parameters
----------
array : numpy.array
Raw data array.
filter_func : function
Function that takes array and returns true or false for each item in array.
converter_func : function
Function that takes array and returns an array or tuple of arrays.
Returns
-------
data_array : numpy.array
Data numpy array of specified dimension (converter_func) and content (filter_func)
'''
# if filter_func != None:
# if not hasattr(filter_func, '__call__'):
# raise ValueError('Filter is not callable')
if filter_func:
array = array[filter_func(array)]
# if converter_func != None:
# if not hasattr(converter_func, '__call__'):
# raise ValueError('Converter is not callable')
if converter_func:
array = converter_func(array)
return array
|
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converter_func : function
Function that takes array and returns an array or tuple of arrays.
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/daq/readout_utils.py#L52-L79
|
SiLab-Bonn/pyBAR
|
pybar/daq/readout_utils.py
|
convert_data_iterable
|
def convert_data_iterable(data_iterable, filter_func=None, converter_func=None): # TODO: add concatenate parameter
'''Convert raw data in data iterable.
Parameters
----------
data_iterable : iterable
Iterable where each element is a tuple with following content: (raw data, timestamp_start, timestamp_stop, status).
filter_func : function
Function that takes array and returns true or false for each item in array.
converter_func : function
Function that takes array and returns an array or tuple of arrays.
Returns
-------
data_list : list
Data list of the form [(converted data, timestamp_start, timestamp_stop, status), (...), ...]
'''
data_list = []
for item in data_iterable:
data_list.append((convert_data_array(item[0], filter_func=filter_func, converter_func=converter_func), item[1], item[2], item[3]))
return data_list
|
python
|
def convert_data_iterable(data_iterable, filter_func=None, converter_func=None): # TODO: add concatenate parameter
'''Convert raw data in data iterable.
Parameters
----------
data_iterable : iterable
Iterable where each element is a tuple with following content: (raw data, timestamp_start, timestamp_stop, status).
filter_func : function
Function that takes array and returns true or false for each item in array.
converter_func : function
Function that takes array and returns an array or tuple of arrays.
Returns
-------
data_list : list
Data list of the form [(converted data, timestamp_start, timestamp_stop, status), (...), ...]
'''
data_list = []
for item in data_iterable:
data_list.append((convert_data_array(item[0], filter_func=filter_func, converter_func=converter_func), item[1], item[2], item[3]))
return data_list
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/daq/readout_utils.py#L82-L102
|
SiLab-Bonn/pyBAR
|
pybar/daq/readout_utils.py
|
data_array_from_data_iterable
|
def data_array_from_data_iterable(data_iterable):
'''Convert data iterable to raw data numpy array.
Parameters
----------
data_iterable : iterable
Iterable where each element is a tuple with following content: (raw data, timestamp_start, timestamp_stop, status).
Returns
-------
data_array : numpy.array
concatenated data array
'''
try:
data_array = np.concatenate([item[0] for item in data_iterable])
except ValueError: # length is 0
data_array = np.empty(0, dtype=np.uint32)
return data_array
|
python
|
def data_array_from_data_iterable(data_iterable):
'''Convert data iterable to raw data numpy array.
Parameters
----------
data_iterable : iterable
Iterable where each element is a tuple with following content: (raw data, timestamp_start, timestamp_stop, status).
Returns
-------
data_array : numpy.array
concatenated data array
'''
try:
data_array = np.concatenate([item[0] for item in data_iterable])
except ValueError: # length is 0
data_array = np.empty(0, dtype=np.uint32)
return data_array
|
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train
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https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/daq/readout_utils.py#L105-L122
|
SiLab-Bonn/pyBAR
|
pybar/daq/readout_utils.py
|
convert_tdc_to_channel
|
def convert_tdc_to_channel(channel):
''' Converts TDC words at a given channel to common TDC header (0x4).
'''
def f(value):
filter_func = logical_and(is_tdc_word, is_tdc_from_channel(channel))
select = filter_func(value)
value[select] = np.bitwise_and(value[select], 0x0FFFFFFF)
value[select] = np.bitwise_or(value[select], 0x40000000)
f.__name__ = "convert_tdc_to_channel_" + str(channel)
return value
return f
|
python
|
def convert_tdc_to_channel(channel):
''' Converts TDC words at a given channel to common TDC header (0x4).
'''
def f(value):
filter_func = logical_and(is_tdc_word, is_tdc_from_channel(channel))
select = filter_func(value)
value[select] = np.bitwise_and(value[select], 0x0FFFFFFF)
value[select] = np.bitwise_or(value[select], 0x40000000)
f.__name__ = "convert_tdc_to_channel_" + str(channel)
return value
return f
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/daq/readout_utils.py#L151-L161
|
SiLab-Bonn/pyBAR
|
pybar/daq/readout_utils.py
|
is_data_from_channel
|
def is_data_from_channel(channel=4): # function factory
'''Selecting FE data from given channel.
Parameters
----------
channel : int
Channel number (4 is default channel on Single Chip Card).
Returns
-------
Function.
Usage:
1 Selecting FE data from channel 4 (combine with is_fe_word):
filter_fe_data_from_channel_4 = logical_and(is_fe_word, is_data_from_channel(4))
fe_data_from_channel_4 = data_array[filter_fe_data_from_channel_4(data_array)]
2 Sleceting data from channel 4:
filter_data_from_channel_4 = is_data_from_channel(4)
data_from_channel_4 = data_array[filter_data_from_channel_4(fe_data_array)]
3 Sleceting data from channel 4:
data_from_channel_4 = is_data_from_channel(4)(fe_raw_data)
Other usage:
f_ch4 = functoools.partial(is_data_from_channel, channel=4)
l_ch4 = lambda x: is_data_from_channel(x, channel=4)
'''
if channel >= 0 and channel < 16:
def f(value):
return np.equal(np.right_shift(np.bitwise_and(value, 0x0F000000), 24), channel)
f.__name__ = "is_data_from_channel_" + str(channel) # or use inspect module: inspect.stack()[0][3]
return f
else:
raise ValueError('Invalid channel number')
|
python
|
def is_data_from_channel(channel=4): # function factory
'''Selecting FE data from given channel.
Parameters
----------
channel : int
Channel number (4 is default channel on Single Chip Card).
Returns
-------
Function.
Usage:
1 Selecting FE data from channel 4 (combine with is_fe_word):
filter_fe_data_from_channel_4 = logical_and(is_fe_word, is_data_from_channel(4))
fe_data_from_channel_4 = data_array[filter_fe_data_from_channel_4(data_array)]
2 Sleceting data from channel 4:
filter_data_from_channel_4 = is_data_from_channel(4)
data_from_channel_4 = data_array[filter_data_from_channel_4(fe_data_array)]
3 Sleceting data from channel 4:
data_from_channel_4 = is_data_from_channel(4)(fe_raw_data)
Other usage:
f_ch4 = functoools.partial(is_data_from_channel, channel=4)
l_ch4 = lambda x: is_data_from_channel(x, channel=4)
'''
if channel >= 0 and channel < 16:
def f(value):
return np.equal(np.right_shift(np.bitwise_and(value, 0x0F000000), 24), channel)
f.__name__ = "is_data_from_channel_" + str(channel) # or use inspect module: inspect.stack()[0][3]
return f
else:
raise ValueError('Invalid channel number')
|
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Selecting FE data from given channel.
Parameters
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Channel number (4 is default channel on Single Chip Card).
Returns
-------
Function.
Usage:
1 Selecting FE data from channel 4 (combine with is_fe_word):
filter_fe_data_from_channel_4 = logical_and(is_fe_word, is_data_from_channel(4))
fe_data_from_channel_4 = data_array[filter_fe_data_from_channel_4(data_array)]
2 Sleceting data from channel 4:
filter_data_from_channel_4 = is_data_from_channel(4)
data_from_channel_4 = data_array[filter_data_from_channel_4(fe_data_array)]
3 Sleceting data from channel 4:
data_from_channel_4 = is_data_from_channel(4)(fe_raw_data)
Other usage:
f_ch4 = functoools.partial(is_data_from_channel, channel=4)
l_ch4 = lambda x: is_data_from_channel(x, channel=4)
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/daq/readout_utils.py#L164-L196
|
SiLab-Bonn/pyBAR
|
pybar/daq/readout_utils.py
|
logical_and
|
def logical_and(f1, f2): # function factory
'''Logical and from functions.
Parameters
----------
f1, f2 : function
Function that takes array and returns true or false for each item in array.
Returns
-------
Function.
Usage:
filter_func=logical_and(is_data_record, is_data_from_channel(4)) # new filter function
filter_func(array) # array that has Data Records from channel 4
'''
def f(value):
return np.logical_and(f1(value), f2(value))
f.__name__ = "(" + f1.__name__ + "_and_" + f2.__name__ + ")"
return f
|
python
|
def logical_and(f1, f2): # function factory
'''Logical and from functions.
Parameters
----------
f1, f2 : function
Function that takes array and returns true or false for each item in array.
Returns
-------
Function.
Usage:
filter_func=logical_and(is_data_record, is_data_from_channel(4)) # new filter function
filter_func(array) # array that has Data Records from channel 4
'''
def f(value):
return np.logical_and(f1(value), f2(value))
f.__name__ = "(" + f1.__name__ + "_and_" + f2.__name__ + ")"
return f
|
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Logical and from functions.
Parameters
----------
f1, f2 : function
Function that takes array and returns true or false for each item in array.
Returns
-------
Function.
Usage:
filter_func=logical_and(is_data_record, is_data_from_channel(4)) # new filter function
filter_func(array) # array that has Data Records from channel 4
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/daq/readout_utils.py#L199-L218
|
SiLab-Bonn/pyBAR
|
pybar/daq/readout_utils.py
|
logical_or
|
def logical_or(f1, f2): # function factory
'''Logical or from functions.
Parameters
----------
f1, f2 : function
Function that takes array and returns true or false for each item in array.
Returns
-------
Function.
'''
def f(value):
return np.logical_or(f1(value), f2(value))
f.__name__ = "(" + f1.__name__ + "_or_" + f2.__name__ + ")"
return f
|
python
|
def logical_or(f1, f2): # function factory
'''Logical or from functions.
Parameters
----------
f1, f2 : function
Function that takes array and returns true or false for each item in array.
Returns
-------
Function.
'''
def f(value):
return np.logical_or(f1(value), f2(value))
f.__name__ = "(" + f1.__name__ + "_or_" + f2.__name__ + ")"
return f
|
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Logical or from functions.
Parameters
----------
f1, f2 : function
Function that takes array and returns true or false for each item in array.
Returns
-------
Function.
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/daq/readout_utils.py#L221-L236
|
SiLab-Bonn/pyBAR
|
pybar/daq/readout_utils.py
|
logical_not
|
def logical_not(f): # function factory
'''Logical not from functions.
Parameters
----------
f1, f2 : function
Function that takes array and returns true or false for each item in array.
Returns
-------
Function.
'''
def f(value):
return np.logical_not(f(value))
f.__name__ = "not_" + f.__name__
return f
|
python
|
def logical_not(f): # function factory
'''Logical not from functions.
Parameters
----------
f1, f2 : function
Function that takes array and returns true or false for each item in array.
Returns
-------
Function.
'''
def f(value):
return np.logical_not(f(value))
f.__name__ = "not_" + f.__name__
return f
|
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Logical not from functions.
Parameters
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f1, f2 : function
Function that takes array and returns true or false for each item in array.
Returns
-------
Function.
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/daq/readout_utils.py#L239-L254
|
SiLab-Bonn/pyBAR
|
pybar/daq/readout_utils.py
|
logical_xor
|
def logical_xor(f1, f2): # function factory
'''Logical xor from functions.
Parameters
----------
f1, f2 : function
Function that takes array and returns true or false for each item in array.
Returns
-------
Function.
'''
def f(value):
return np.logical_xor(f1(value), f2(value))
f.__name__ = "(" + f1.__name__ + "_xor_" + f2.__name__ + ")"
return f
|
python
|
def logical_xor(f1, f2): # function factory
'''Logical xor from functions.
Parameters
----------
f1, f2 : function
Function that takes array and returns true or false for each item in array.
Returns
-------
Function.
'''
def f(value):
return np.logical_xor(f1(value), f2(value))
f.__name__ = "(" + f1.__name__ + "_xor_" + f2.__name__ + ")"
return f
|
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Function that takes array and returns true or false for each item in array.
Returns
-------
Function.
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/daq/readout_utils.py#L257-L272
|
SiLab-Bonn/pyBAR
|
pybar/daq/readout_utils.py
|
get_trigger_data
|
def get_trigger_data(value, mode=0):
'''Returns 31bit trigger counter (mode=0), 31bit timestamp (mode=1), 15bit timestamp and 16bit trigger counter (mode=2)
'''
if mode == 2:
return np.right_shift(np.bitwise_and(value, 0x7FFF0000), 16), np.bitwise_and(value, 0x0000FFFF)
else:
return np.bitwise_and(value, 0x7FFFFFFF)
|
python
|
def get_trigger_data(value, mode=0):
'''Returns 31bit trigger counter (mode=0), 31bit timestamp (mode=1), 15bit timestamp and 16bit trigger counter (mode=2)
'''
if mode == 2:
return np.right_shift(np.bitwise_and(value, 0x7FFF0000), 16), np.bitwise_and(value, 0x0000FFFF)
else:
return np.bitwise_and(value, 0x7FFFFFFF)
|
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Returns 31bit trigger counter (mode=0), 31bit timestamp (mode=1), 15bit timestamp and 16bit trigger counter (mode=2)
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/daq/readout_utils.py#L315-L321
|
SiLab-Bonn/pyBAR
|
pybar/daq/readout_utils.py
|
get_col_row_tot_array_from_data_record_array
|
def get_col_row_tot_array_from_data_record_array(array): # TODO: max ToT
'''Convert raw data array to column, row, and ToT array.
Parameters
----------
array : numpy.array
Raw data array.
Returns
-------
Tuple of arrays.
'''
def get_col_row_tot_1_array_from_data_record_array(value):
return np.right_shift(np.bitwise_and(value, 0x00FE0000), 17), np.right_shift(np.bitwise_and(value, 0x0001FF00), 8), np.right_shift(np.bitwise_and(value, 0x000000F0), 4)
def get_col_row_tot_2_array_from_data_record_array(value):
return np.right_shift(np.bitwise_and(value, 0x00FE0000), 17), np.add(np.right_shift(np.bitwise_and(value, 0x0001FF00), 8), 1), np.bitwise_and(value, 0x0000000F)
col_row_tot_1_array = np.column_stack(get_col_row_tot_1_array_from_data_record_array(array))
col_row_tot_2_array = np.column_stack(get_col_row_tot_2_array_from_data_record_array(array))
# interweave array here
col_row_tot_array = np.vstack((col_row_tot_1_array.T, col_row_tot_2_array.T)).reshape((3, -1), order='F').T # http://stackoverflow.com/questions/5347065/interweaving-two-numpy-arrays
# remove ToT > 14 (late hit, no hit) from array, remove row > 336 in case we saw hit in row 336 (no double hit possible)
try:
col_row_tot_array_filtered = col_row_tot_array[col_row_tot_array[:, 2] < 14] # [np.logical_and(col_row_tot_array[:,2]<14, col_row_tot_array[:,1]<=336)]
except IndexError:
# logging.warning('Array is empty')
return np.array([], dtype=np.dtype('>u4')), np.array([], dtype=np.dtype('>u4')), np.array([], dtype=np.dtype('>u4'))
return col_row_tot_array_filtered[:, 0], col_row_tot_array_filtered[:, 1], col_row_tot_array_filtered[:, 2]
|
python
|
def get_col_row_tot_array_from_data_record_array(array): # TODO: max ToT
'''Convert raw data array to column, row, and ToT array.
Parameters
----------
array : numpy.array
Raw data array.
Returns
-------
Tuple of arrays.
'''
def get_col_row_tot_1_array_from_data_record_array(value):
return np.right_shift(np.bitwise_and(value, 0x00FE0000), 17), np.right_shift(np.bitwise_and(value, 0x0001FF00), 8), np.right_shift(np.bitwise_and(value, 0x000000F0), 4)
def get_col_row_tot_2_array_from_data_record_array(value):
return np.right_shift(np.bitwise_and(value, 0x00FE0000), 17), np.add(np.right_shift(np.bitwise_and(value, 0x0001FF00), 8), 1), np.bitwise_and(value, 0x0000000F)
col_row_tot_1_array = np.column_stack(get_col_row_tot_1_array_from_data_record_array(array))
col_row_tot_2_array = np.column_stack(get_col_row_tot_2_array_from_data_record_array(array))
# interweave array here
col_row_tot_array = np.vstack((col_row_tot_1_array.T, col_row_tot_2_array.T)).reshape((3, -1), order='F').T # http://stackoverflow.com/questions/5347065/interweaving-two-numpy-arrays
# remove ToT > 14 (late hit, no hit) from array, remove row > 336 in case we saw hit in row 336 (no double hit possible)
try:
col_row_tot_array_filtered = col_row_tot_array[col_row_tot_array[:, 2] < 14] # [np.logical_and(col_row_tot_array[:,2]<14, col_row_tot_array[:,1]<=336)]
except IndexError:
# logging.warning('Array is empty')
return np.array([], dtype=np.dtype('>u4')), np.array([], dtype=np.dtype('>u4')), np.array([], dtype=np.dtype('>u4'))
return col_row_tot_array_filtered[:, 0], col_row_tot_array_filtered[:, 1], col_row_tot_array_filtered[:, 2]
|
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Convert raw data array to column, row, and ToT array.
Parameters
----------
array : numpy.array
Raw data array.
Returns
-------
Tuple of arrays.
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/daq/readout_utils.py#L342-L370
|
SiLab-Bonn/pyBAR
|
pybar/daq/readout_utils.py
|
interpret_pixel_data
|
def interpret_pixel_data(data, dc, pixel_array, invert=True):
'''Takes the pixel raw data and interprets them. This includes consistency checks and pixel/data matching.
The data has to come from one double column only but can have more than one pixel bit (e.g. TDAC = 5 bit).
Parameters
----------
data : numpy.ndarray
The raw data words.
dc : int
The double column where the data is from.
pixel_array : numpy.ma.ndarray
The masked numpy.ndarrays to be filled. The masked is set to zero for pixels with valid data.
invert : boolean
Invert the read pixel data.
'''
# data validity cut, VR has to follow an AR
index_value = np.where(is_address_record(data))[0] + 1 # assume value record follows address record
index_value = index_value[is_value_record(data[index_value])] # delete all non value records
index_address = index_value - 1 # calculate address record indices that are followed by an value record
# create the pixel address/value arrays
address = get_address_record_address(data[index_address])
value = get_value_record(data[index_address + 1])
# split array for each bit in pixel data, split is done on decreasing address values
address_split = np.array_split(address, np.where(np.diff(address.astype(np.int32)) < 0)[0] + 1)
value_split = np.array_split(value, np.where(np.diff(address.astype(np.int32)) < 0)[0] + 1)
if len(address_split) > 5:
pixel_array.mask[dc * 2, :] = True
pixel_array.mask[dc * 2 + 1, :] = True
logging.warning('Invalid pixel data for DC %d', dc)
return
mask = np.empty_like(pixel_array.data) # BUG in numpy: pixel_array is de-masked if not .data is used
mask[:] = len(address_split)
for bit, (bit_address, bit_value) in enumerate(zip(address_split, value_split)): # loop over all bits of the pixel data
# error output, pixel data is often corrupt for FE-I4A
if len(bit_address) == 0:
logging.warning('No pixel data for DC %d', dc)
continue
if len(bit_address) != 42:
logging.warning('Some pixel data missing for DC %d', dc)
if (np.any(bit_address > 672)):
RuntimeError('Pixel data corrupt for DC %d', dc)
# set pixel that occurred in the data stream
pixel = []
for i in bit_address:
pixel.extend(range(i - 15, i + 1))
pixel = np.array(pixel)
# create bit set array
value_new = bit_value.view(np.uint8) # interpret 32 bit numpy array as uint8 to be able to use bit unpacking; byte unpacking is not supported yet
if invert:
value_new = np.invert(value_new) # read back values are inverted
value_new = np.insert(value_new[::4], np.arange(len(value_new[1::4])), value_new[1::4]) # delete 0 padding
value_bit = np.unpackbits(value_new, axis=0)
if len(address_split) == 5: # detect TDAC data, here the bit order is flipped
bit_set = len(address_split) - bit - 1
else:
bit_set = bit
pixel_array.data[dc * 2, pixel[pixel >= 336] - 336] = np.bitwise_or(pixel_array.data[dc * 2, pixel[pixel >= 336] - 336], np.left_shift(value_bit[pixel >= 336], bit_set))
pixel_array.data[dc * 2 + 1, pixel[pixel < 336]] = np.bitwise_or(pixel_array.data[dc * 2 + 1, pixel[pixel < 336]], np.left_shift(value_bit[pixel < 336], bit_set)[::-1])
mask[dc * 2, pixel[pixel >= 336] - 336] = mask[dc * 2, pixel[pixel >= 336] - 336] - 1
mask[dc * 2 + 1, pixel[pixel < 336]] = mask[dc * 2 + 1, pixel[pixel < 336]] - 1
pixel_array.mask[np.equal(mask, 0)] = False
|
python
|
def interpret_pixel_data(data, dc, pixel_array, invert=True):
'''Takes the pixel raw data and interprets them. This includes consistency checks and pixel/data matching.
The data has to come from one double column only but can have more than one pixel bit (e.g. TDAC = 5 bit).
Parameters
----------
data : numpy.ndarray
The raw data words.
dc : int
The double column where the data is from.
pixel_array : numpy.ma.ndarray
The masked numpy.ndarrays to be filled. The masked is set to zero for pixels with valid data.
invert : boolean
Invert the read pixel data.
'''
# data validity cut, VR has to follow an AR
index_value = np.where(is_address_record(data))[0] + 1 # assume value record follows address record
index_value = index_value[is_value_record(data[index_value])] # delete all non value records
index_address = index_value - 1 # calculate address record indices that are followed by an value record
# create the pixel address/value arrays
address = get_address_record_address(data[index_address])
value = get_value_record(data[index_address + 1])
# split array for each bit in pixel data, split is done on decreasing address values
address_split = np.array_split(address, np.where(np.diff(address.astype(np.int32)) < 0)[0] + 1)
value_split = np.array_split(value, np.where(np.diff(address.astype(np.int32)) < 0)[0] + 1)
if len(address_split) > 5:
pixel_array.mask[dc * 2, :] = True
pixel_array.mask[dc * 2 + 1, :] = True
logging.warning('Invalid pixel data for DC %d', dc)
return
mask = np.empty_like(pixel_array.data) # BUG in numpy: pixel_array is de-masked if not .data is used
mask[:] = len(address_split)
for bit, (bit_address, bit_value) in enumerate(zip(address_split, value_split)): # loop over all bits of the pixel data
# error output, pixel data is often corrupt for FE-I4A
if len(bit_address) == 0:
logging.warning('No pixel data for DC %d', dc)
continue
if len(bit_address) != 42:
logging.warning('Some pixel data missing for DC %d', dc)
if (np.any(bit_address > 672)):
RuntimeError('Pixel data corrupt for DC %d', dc)
# set pixel that occurred in the data stream
pixel = []
for i in bit_address:
pixel.extend(range(i - 15, i + 1))
pixel = np.array(pixel)
# create bit set array
value_new = bit_value.view(np.uint8) # interpret 32 bit numpy array as uint8 to be able to use bit unpacking; byte unpacking is not supported yet
if invert:
value_new = np.invert(value_new) # read back values are inverted
value_new = np.insert(value_new[::4], np.arange(len(value_new[1::4])), value_new[1::4]) # delete 0 padding
value_bit = np.unpackbits(value_new, axis=0)
if len(address_split) == 5: # detect TDAC data, here the bit order is flipped
bit_set = len(address_split) - bit - 1
else:
bit_set = bit
pixel_array.data[dc * 2, pixel[pixel >= 336] - 336] = np.bitwise_or(pixel_array.data[dc * 2, pixel[pixel >= 336] - 336], np.left_shift(value_bit[pixel >= 336], bit_set))
pixel_array.data[dc * 2 + 1, pixel[pixel < 336]] = np.bitwise_or(pixel_array.data[dc * 2 + 1, pixel[pixel < 336]], np.left_shift(value_bit[pixel < 336], bit_set)[::-1])
mask[dc * 2, pixel[pixel >= 336] - 336] = mask[dc * 2, pixel[pixel >= 336] - 336] - 1
mask[dc * 2 + 1, pixel[pixel < 336]] = mask[dc * 2 + 1, pixel[pixel < 336]] - 1
pixel_array.mask[np.equal(mask, 0)] = False
|
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Parameters
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data : numpy.ndarray
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pixel_array : numpy.ma.ndarray
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/daq/readout_utils.py#L428-L499
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analyze_raw_data.py
|
AnalyzeRawData.set_standard_settings
|
def set_standard_settings(self):
'''Set all settings to their standard values.
'''
if self.is_open(self.out_file_h5):
self.out_file_h5.close()
self.out_file_h5 = None
self._setup_clusterizer()
self.chunk_size = 3000000
self.n_injections = None
self.trig_count = 0 # 0 trig_count = 16 BCID per trigger
self.max_tot_value = 13
self.vcal_c0, self.vcal_c1 = None, None
self.c_low, self.c_mid, self.c_high = None, None, None
self.c_low_mask, self.c_high_mask = None, None
self._filter_table = tb.Filters(complib='blosc', complevel=5, fletcher32=False)
warnings.simplefilter("ignore", OptimizeWarning)
self.meta_event_index = None
self.fei4b = False
self.create_hit_table = False
self.create_empty_event_hits = False
self.create_meta_event_index = True
self.create_tot_hist = True
self.create_mean_tot_hist = False
self.create_tot_pixel_hist = True
self.create_rel_bcid_hist = True
self.correct_corrupted_data = False
self.create_error_hist = True
self.create_service_record_hist = True
self.create_occupancy_hist = True
self.create_meta_word_index = False
self.create_source_scan_hist = False
self.create_tdc_hist = False
self.create_tdc_counter_hist = False
self.create_tdc_pixel_hist = False
self.create_trigger_error_hist = False
self.create_threshold_hists = False
self.create_threshold_mask = True # Threshold/noise histogram mask: masking all pixels out of bounds
self.create_fitted_threshold_mask = True # Fitted threshold/noise histogram mask: masking all pixels out of bounds
self.create_fitted_threshold_hists = False
self.create_cluster_hit_table = False
self.create_cluster_table = False
self.create_cluster_size_hist = False
self.create_cluster_tot_hist = False
self.align_at_trigger = False # use the trigger word to align the events
self.align_at_tdc = False # use the trigger word to align the events
self.trigger_data_format = 0 # 0: 31bit trigger number, 1: 31bit trigger time stamp, 2: 15bit trigger time stamp + 16bit trigger number
self.use_tdc_trigger_time_stamp = False # the tdc time stamp is the difference between trigger and tdc rising edge
self.max_tdc_delay = 255
self.max_trigger_number = 2 ** 16 - 1
self.set_stop_mode = False
|
python
|
def set_standard_settings(self):
'''Set all settings to their standard values.
'''
if self.is_open(self.out_file_h5):
self.out_file_h5.close()
self.out_file_h5 = None
self._setup_clusterizer()
self.chunk_size = 3000000
self.n_injections = None
self.trig_count = 0 # 0 trig_count = 16 BCID per trigger
self.max_tot_value = 13
self.vcal_c0, self.vcal_c1 = None, None
self.c_low, self.c_mid, self.c_high = None, None, None
self.c_low_mask, self.c_high_mask = None, None
self._filter_table = tb.Filters(complib='blosc', complevel=5, fletcher32=False)
warnings.simplefilter("ignore", OptimizeWarning)
self.meta_event_index = None
self.fei4b = False
self.create_hit_table = False
self.create_empty_event_hits = False
self.create_meta_event_index = True
self.create_tot_hist = True
self.create_mean_tot_hist = False
self.create_tot_pixel_hist = True
self.create_rel_bcid_hist = True
self.correct_corrupted_data = False
self.create_error_hist = True
self.create_service_record_hist = True
self.create_occupancy_hist = True
self.create_meta_word_index = False
self.create_source_scan_hist = False
self.create_tdc_hist = False
self.create_tdc_counter_hist = False
self.create_tdc_pixel_hist = False
self.create_trigger_error_hist = False
self.create_threshold_hists = False
self.create_threshold_mask = True # Threshold/noise histogram mask: masking all pixels out of bounds
self.create_fitted_threshold_mask = True # Fitted threshold/noise histogram mask: masking all pixels out of bounds
self.create_fitted_threshold_hists = False
self.create_cluster_hit_table = False
self.create_cluster_table = False
self.create_cluster_size_hist = False
self.create_cluster_tot_hist = False
self.align_at_trigger = False # use the trigger word to align the events
self.align_at_tdc = False # use the trigger word to align the events
self.trigger_data_format = 0 # 0: 31bit trigger number, 1: 31bit trigger time stamp, 2: 15bit trigger time stamp + 16bit trigger number
self.use_tdc_trigger_time_stamp = False # the tdc time stamp is the difference between trigger and tdc rising edge
self.max_tdc_delay = 255
self.max_trigger_number = 2 ** 16 - 1
self.set_stop_mode = False
|
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Set all settings to their standard values.
|
[
"Set",
"all",
"settings",
"to",
"their",
"standard",
"values",
"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analyze_raw_data.py#L263-L312
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analyze_raw_data.py
|
AnalyzeRawData.trig_count
|
def trig_count(self, value):
"""Set the numbers of BCIDs (usually 16) of one event."""
self._trig_count = 16 if value == 0 else value
self.interpreter.set_trig_count(self._trig_count)
|
python
|
def trig_count(self, value):
"""Set the numbers of BCIDs (usually 16) of one event."""
self._trig_count = 16 if value == 0 else value
self.interpreter.set_trig_count(self._trig_count)
|
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Set the numbers of BCIDs (usually 16) of one event.
|
[
"Set",
"the",
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"of",
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"(",
"usually",
"16",
")",
"of",
"one",
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"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analyze_raw_data.py#L531-L534
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analyze_raw_data.py
|
AnalyzeRawData.max_tot_value
|
def max_tot_value(self, value):
"""Set maximum ToT value that is considered to be a hit"""
self._max_tot_value = value
self.interpreter.set_max_tot(self._max_tot_value)
self.histogram.set_max_tot(self._max_tot_value)
self.clusterizer.set_max_hit_charge(self._max_tot_value)
|
python
|
def max_tot_value(self, value):
"""Set maximum ToT value that is considered to be a hit"""
self._max_tot_value = value
self.interpreter.set_max_tot(self._max_tot_value)
self.histogram.set_max_tot(self._max_tot_value)
self.clusterizer.set_max_hit_charge(self._max_tot_value)
|
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Set maximum ToT value that is considered to be a hit
|
[
"Set",
"maximum",
"ToT",
"value",
"that",
"is",
"considered",
"to",
"be",
"a",
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] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analyze_raw_data.py#L542-L547
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analyze_raw_data.py
|
AnalyzeRawData.interpret_word_table
|
def interpret_word_table(self, analyzed_data_file=None, use_settings_from_file=True, fei4b=None):
'''Interprets the raw data word table of all given raw data files with the c++ library.
Creates the h5 output file and PDF plots.
Parameters
----------
analyzed_data_file : string
The file name of the output analyzed data file. If None, the output analyzed data file
specified during initialization is taken.
use_settings_from_file : boolean
True if the needed parameters should be extracted from the raw data file
fei4b : boolean
True if the raw data is from FE-I4B.
'''
logging.info('Interpreting raw data file(s): ' + (', ').join(self.files_dict.keys()))
if self._create_meta_word_index:
meta_word = np.empty((self._chunk_size,), dtype=dtype_from_descr(data_struct.MetaInfoWordTable))
self.interpreter.set_meta_data_word_index(meta_word)
self.interpreter.reset_event_variables()
self.interpreter.reset_counters()
self.meta_data = analysis_utils.combine_meta_data(self.files_dict, meta_data_v2=self.interpreter.meta_table_v2)
if self.meta_data is None or self.meta_data.shape[0] == 0:
raise analysis_utils.IncompleteInputError('Meta data is empty. Stopping interpretation.')
self.interpreter.set_meta_data(self.meta_data) # tell interpreter the word index per readout to be able to calculate the event number per read out
meta_data_size = self.meta_data.shape[0]
self.meta_event_index = np.zeros((meta_data_size,), dtype=[('metaEventIndex', np.uint64)]) # this array is filled by the interpreter and holds the event number per read out
self.interpreter.set_meta_event_data(self.meta_event_index) # tell the interpreter the data container to write the meta event index to
if self.scan_parameters is None:
self.histogram.set_no_scan_parameter()
else:
self.scan_parameter_index = analysis_utils.get_scan_parameters_index(self.scan_parameters) # a array that labels unique scan parameter combinations
self.histogram.add_scan_parameter(self.scan_parameter_index) # just add an index for the different scan parameter combinations
if self._create_cluster_size_hist: # Cluster size result histogram
self._cluster_size_hist = np.zeros(shape=(6, ), dtype=np.uint32)
if self._create_cluster_tot_hist: # Cluster tot/size result histogram
self._cluster_tot_hist = np.zeros(shape=(16, 6), dtype=np.uint32)
close_analyzed_data_file = False
if analyzed_data_file is not None: # if an output file name is specified create new file for analyzed data
if self.is_open(self.out_file_h5) and os.path.abspath(analyzed_data_file) == os.path.abspath(self.out_file_h5.filename):
out_file_h5 = self.out_file_h5
else:
# normalize path
analyzed_data_file = os.path.abspath(analyzed_data_file)
if os.path.splitext(analyzed_data_file)[1].lower() != ".h5":
analyzed_data_file = os.path.splitext(analyzed_data_file)[0] + ".h5"
out_file_h5 = tb.open_file(analyzed_data_file, mode="w", title="Interpreted FE-I4 raw data")
close_analyzed_data_file = True
elif self.is_open(self.out_file_h5):
out_file_h5 = self.out_file_h5
else:
out_file_h5 = None
tmp_out_file_h5 = self.out_file_h5
if not self.is_open(self.out_file_h5) and self.is_open(out_file_h5):
close_analyzed_data_file = False
tmp_out_file_h5 = out_file_h5
self.out_file_h5 = out_file_h5
if self.is_open(self.out_file_h5):
self._analyzed_data_file = self.out_file_h5.filename
else:
self._analyzed_data_file is None
if self._analyzed_data_file is not None:
if self._create_hit_table is True:
description = data_struct.HitInfoTable().columns.copy()
hit_table = self.out_file_h5.create_table(self.out_file_h5.root, name='Hits', description=description, title='hit_data', filters=self._filter_table, chunkshape=(self._chunk_size / 100,))
if self._create_meta_word_index is True:
meta_word_index_table = self.out_file_h5.create_table(self.out_file_h5.root, name='EventMetaData', description=data_struct.MetaInfoWordTable, title='event_meta_data', filters=self._filter_table, chunkshape=(self._chunk_size / 10,))
if self._create_cluster_table:
cluster_table = self.out_file_h5.create_table(self.out_file_h5.root, name='Cluster', description=data_struct.ClusterInfoTable, title='Cluster data', filters=self._filter_table, expectedrows=self._chunk_size)
if self._create_cluster_hit_table:
description = data_struct.ClusterHitInfoTable().columns.copy()
cluster_hit_table = self.out_file_h5.create_table(self.out_file_h5.root, name='ClusterHits', description=description, title='cluster_hit_data', filters=self._filter_table, expectedrows=self._chunk_size)
logging.info("Interpreting raw data...")
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.AdaptiveETA()], maxval=analysis_utils.get_total_n_data_words(self.files_dict), term_width=80)
progress_bar.start()
total_words = 0
for file_index, raw_data_file in enumerate(self.files_dict.keys()): # loop over all raw data files
self.interpreter.reset_meta_data_counter()
with tb.open_file(raw_data_file, mode="r") as in_file_h5:
if use_settings_from_file:
self._deduce_settings_from_file(in_file_h5)
else:
self.fei4b = fei4b
if self.interpreter.meta_table_v2:
index_start = in_file_h5.root.meta_data.read(field='index_start')
index_stop = in_file_h5.root.meta_data.read(field='index_stop')
else:
index_start = in_file_h5.root.meta_data.read(field='start_index')
index_stop = in_file_h5.root.meta_data.read(field='stop_index')
bad_word_index = set()
# Check for bad data
if self._correct_corrupted_data:
tw = 2147483648 # trigger word
dh = 15269888 # data header
is_fe_data_header = logical_and(is_fe_word, is_data_header)
found_first_trigger = False
readout_slices = np.column_stack((index_start, index_stop))
previous_prepend_data_headers = None
prepend_data_headers = None
last_good_readout_index = None
last_index_with_event_data = None
for read_out_index, (index_start, index_stop) in enumerate(readout_slices):
try:
raw_data = in_file_h5.root.raw_data.read(index_start, index_stop)
except OverflowError, e:
pass
except tb.exceptions.HDF5ExtError:
break
# previous data chunk had bad data, check for good data
if (index_start - 1) in bad_word_index:
bad_data, current_prepend_data_headers, _ , _ = check_bad_data(raw_data, prepend_data_headers=1, trig_count=None)
if bad_data:
bad_word_index = bad_word_index.union(range(index_start, index_stop))
else:
# logging.info("found good data in %s from index %d to %d (chunk %d, length %d)" % (in_file_h5.filename, index_start, index_stop, read_out_index, (index_stop - index_start)))
if last_good_readout_index + 1 == read_out_index - 1:
logging.warning("found bad data in %s from index %d to %d (chunk %d, length %d)" % (in_file_h5.filename, readout_slices[last_good_readout_index][1], readout_slices[read_out_index - 1][1], last_good_readout_index + 1, (readout_slices[read_out_index - 1][1] - readout_slices[last_good_readout_index][1])))
else:
logging.warning("found bad data in %s from index %d to %d (chunk %d to %d, length %d)" % (in_file_h5.filename, readout_slices[last_good_readout_index][1], readout_slices[read_out_index - 1][1], last_good_readout_index + 1, read_out_index - 1, (readout_slices[read_out_index - 1][1] - readout_slices[last_good_readout_index][1])))
previous_good_raw_data = in_file_h5.root.raw_data.read(readout_slices[last_good_readout_index][0], readout_slices[last_good_readout_index][1] - 1)
previous_bad_raw_data = in_file_h5.root.raw_data.read(readout_slices[last_good_readout_index][1] - 1, readout_slices[read_out_index - 1][1])
fixed_raw_data, _ = fix_raw_data(previous_bad_raw_data, lsb_byte=None)
fixed_raw_data = np.r_[previous_good_raw_data, fixed_raw_data, raw_data]
_, prepend_data_headers, n_triggers, n_dh = check_bad_data(fixed_raw_data, prepend_data_headers=previous_prepend_data_headers, trig_count=self.trig_count)
last_good_readout_index = read_out_index
if n_triggers != 0 or n_dh != 0:
last_index_with_event_data = read_out_index
last_event_data_prepend_data_headers = prepend_data_headers
fixed_previous_raw_data = np.r_[previous_good_raw_data, fixed_raw_data]
_, previous_prepend_data_headers, _ , _ = check_bad_data(fixed_previous_raw_data, prepend_data_headers=previous_prepend_data_headers, trig_count=self.trig_count)
# check for bad data
else:
# workaround for first data chunk, might have missing trigger in some rare cases (already fixed in firmware)
if read_out_index == 0 and (np.any(is_trigger_word(raw_data) >= 1) or np.any(is_fe_data_header(raw_data) >= 1)):
bad_data, current_prepend_data_headers, n_triggers , n_dh = check_bad_data(raw_data, prepend_data_headers=1, trig_count=None)
# check for full last event in data
if current_prepend_data_headers == self.trig_count:
current_prepend_data_headers = None
# usually check for bad data happens here
else:
bad_data, current_prepend_data_headers, n_triggers , n_dh = check_bad_data(raw_data, prepend_data_headers=prepend_data_headers, trig_count=self.trig_count)
# do additional check with follow up data chunk and decide whether current chunk is defect or not
if bad_data:
if read_out_index == 0:
fixed_raw_data_chunk, _ = fix_raw_data(raw_data, lsb_byte=None)
fixed_raw_data_list = [fixed_raw_data_chunk]
else:
previous_raw_data = in_file_h5.root.raw_data.read(*readout_slices[read_out_index - 1])
raw_data_with_previous_data_word = np.r_[previous_raw_data[-1], raw_data]
fixed_raw_data_chunk, _ = fix_raw_data(raw_data_with_previous_data_word, lsb_byte=None)
fixed_raw_data = np.r_[previous_raw_data[:-1], fixed_raw_data_chunk]
# last data word of chunk before broken chunk migh be a trigger word or data header which cannot be recovered
fixed_raw_data_with_tw = np.r_[previous_raw_data[:-1], tw, fixed_raw_data_chunk]
fixed_raw_data_with_dh = np.r_[previous_raw_data[:-1], dh, fixed_raw_data_chunk]
fixed_raw_data_list = [fixed_raw_data, fixed_raw_data_with_tw, fixed_raw_data_with_dh]
bad_fixed_data, _, _ , _ = check_bad_data(fixed_raw_data_with_dh, prepend_data_headers=previous_prepend_data_headers, trig_count=self.trig_count)
bad_fixed_data = map(lambda data: check_bad_data(data, prepend_data_headers=previous_prepend_data_headers, trig_count=self.trig_count)[0], fixed_raw_data_list)
if not all(bad_fixed_data): # good fixed data
# last word in chunk before currrent chunk is also bad
if index_start != 0:
bad_word_index.add(index_start - 1)
# adding all word from current chunk
bad_word_index = bad_word_index.union(range(index_start, index_stop))
last_good_readout_index = read_out_index - 1
else:
# a previous chunk might be broken and the last data word becomes a trigger word, so do additional checks
if last_index_with_event_data and last_event_data_prepend_data_headers != read_out_index:
before_bad_raw_data = in_file_h5.root.raw_data.read(readout_slices[last_index_with_event_data - 1][0], readout_slices[last_index_with_event_data - 1][1] - 1)
previous_bad_raw_data = in_file_h5.root.raw_data.read(readout_slices[last_index_with_event_data][0] - 1, readout_slices[last_index_with_event_data][1])
fixed_raw_data, _ = fix_raw_data(previous_bad_raw_data, lsb_byte=None)
previous_good_raw_data = in_file_h5.root.raw_data.read(readout_slices[last_index_with_event_data][1], readout_slices[read_out_index - 1][1])
fixed_raw_data = np.r_[before_bad_raw_data, fixed_raw_data, previous_good_raw_data, raw_data]
bad_fixed_previous_data, current_prepend_data_headers, _, _ = check_bad_data(fixed_raw_data, prepend_data_headers=last_event_data_prepend_data_headers, trig_count=self.trig_count)
if not bad_fixed_previous_data:
logging.warning("found bad data in %s from index %d to %d (chunk %d, length %d)" % (in_file_h5.filename, readout_slices[last_index_with_event_data][0], readout_slices[last_index_with_event_data][1], last_index_with_event_data, (readout_slices[last_index_with_event_data][1] - readout_slices[last_index_with_event_data][0])))
bad_word_index = bad_word_index.union(range(readout_slices[last_index_with_event_data][0] - 1, readout_slices[last_index_with_event_data][1]))
else:
logging.warning("found bad data which cannot be corrected in %s from index %d to %d (chunk %d, length %d)" % (in_file_h5.filename, index_start, index_stop, read_out_index, (index_stop - index_start)))
else:
logging.warning("found bad data which cannot be corrected in %s from index %d to %d (chunk %d, length %d)" % (in_file_h5.filename, index_start, index_stop, read_out_index, (index_stop - index_start)))
if n_triggers != 0 or n_dh != 0:
last_index_with_event_data = read_out_index
last_event_data_prepend_data_headers = prepend_data_headers
if not bad_data or (bad_data and bad_fixed_data):
previous_prepend_data_headers = prepend_data_headers
prepend_data_headers = current_prepend_data_headers
consecutive_bad_words_list = consecutive(sorted(bad_word_index))
lsb_byte = None
# Loop over raw data in chunks
for word_index in range(0, in_file_h5.root.raw_data.shape[0], self._chunk_size): # loop over all words in the actual raw data file
try:
raw_data = in_file_h5.root.raw_data.read(word_index, word_index + self._chunk_size)
except OverflowError, e:
logging.error('%s: 2^31 xrange() limitation in 32-bit Python', e)
except tb.exceptions.HDF5ExtError:
logging.warning('Raw data file %s has missing raw data. Continue raw data analysis.', in_file_h5.filename)
break
total_words += raw_data.shape[0]
# fix bad data
if self._correct_corrupted_data:
# increase word shift for every bad data chunk in raw data chunk
word_shift = 0
chunk_indices = np.arange(word_index, word_index + self._chunk_size)
for consecutive_bad_word_indices in consecutive_bad_words_list:
selected_words = np.intersect1d(consecutive_bad_word_indices, chunk_indices, assume_unique=True)
if selected_words.shape[0]:
fixed_raw_data, lsb_byte = fix_raw_data(raw_data[selected_words - word_index - word_shift], lsb_byte=lsb_byte)
raw_data = np.r_[raw_data[:selected_words[0] - word_index - word_shift], fixed_raw_data, raw_data[selected_words[-1] - word_index + 1 - word_shift:]]
# check if last word of bad data chunk in current raw data chunk
if consecutive_bad_word_indices[-1] in selected_words:
lsb_byte = None
# word shift by removing data word at the beginning of each defect chunk
word_shift += 1
# bad data chunk is at the end of current raw data chunk
else:
break
self.interpreter.interpret_raw_data(raw_data) # interpret the raw data
# store remaining buffered event in the interpreter at the end of the last file
if file_index == len(self.files_dict.keys()) - 1 and word_index == range(0, in_file_h5.root.raw_data.shape[0], self._chunk_size)[-1]: # store hits of the latest event of the last file
self.interpreter.store_event()
hits = self.interpreter.get_hits()
if self.scan_parameters is not None:
nEventIndex = self.interpreter.get_n_meta_data_event()
self.histogram.add_meta_event_index(self.meta_event_index, nEventIndex)
if self.is_histogram_hits():
self.histogram_hits(hits)
if self.is_cluster_hits():
cluster_hits, clusters = self.cluster_hits(hits)
if self._create_cluster_hit_table:
cluster_hit_table.append(cluster_hits)
if self._create_cluster_table:
cluster_table.append(clusters)
if self._create_cluster_size_hist:
if clusters['size'].shape[0] > 0 and np.max(clusters['size']) + 1 > self._cluster_size_hist.shape[0]:
self._cluster_size_hist.resize(np.max(clusters['size']) + 1)
self._cluster_size_hist += fast_analysis_utils.hist_1d_index(clusters['size'], shape=self._cluster_size_hist.shape)
if self._create_cluster_tot_hist:
if clusters['tot'].shape[0] > 0 and np.max(clusters['tot']) + 1 > self._cluster_tot_hist.shape[0]:
self._cluster_tot_hist.resize((np.max(clusters['tot']) + 1, self._cluster_tot_hist.shape[1]))
if clusters['size'].shape[0] > 0 and np.max(clusters['size']) + 1 > self._cluster_tot_hist.shape[1]:
self._cluster_tot_hist.resize((self._cluster_tot_hist.shape[0], np.max(clusters['size']) + 1))
self._cluster_tot_hist += fast_analysis_utils.hist_2d_index(clusters['tot'], clusters['size'], shape=self._cluster_tot_hist.shape)
if self._analyzed_data_file is not None and self._create_hit_table:
hit_table.append(hits)
if self._analyzed_data_file is not None and self._create_meta_word_index:
size = self.interpreter.get_n_meta_data_word()
meta_word_index_table.append(meta_word[:size])
if total_words <= progress_bar.maxval: # Otherwise exception is thrown
progress_bar.update(total_words)
self.out_file_h5.flush()
progress_bar.finish()
self._create_additional_data()
if close_analyzed_data_file:
self.out_file_h5.close()
self.out_file_h5 = None
self.out_file_h5 = out_file_h5
if self.is_open(self.out_file_h5):
self._analyzed_data_file = self.out_file_h5.filename
else:
self._analyzed_data_file = None
|
python
|
def interpret_word_table(self, analyzed_data_file=None, use_settings_from_file=True, fei4b=None):
'''Interprets the raw data word table of all given raw data files with the c++ library.
Creates the h5 output file and PDF plots.
Parameters
----------
analyzed_data_file : string
The file name of the output analyzed data file. If None, the output analyzed data file
specified during initialization is taken.
use_settings_from_file : boolean
True if the needed parameters should be extracted from the raw data file
fei4b : boolean
True if the raw data is from FE-I4B.
'''
logging.info('Interpreting raw data file(s): ' + (', ').join(self.files_dict.keys()))
if self._create_meta_word_index:
meta_word = np.empty((self._chunk_size,), dtype=dtype_from_descr(data_struct.MetaInfoWordTable))
self.interpreter.set_meta_data_word_index(meta_word)
self.interpreter.reset_event_variables()
self.interpreter.reset_counters()
self.meta_data = analysis_utils.combine_meta_data(self.files_dict, meta_data_v2=self.interpreter.meta_table_v2)
if self.meta_data is None or self.meta_data.shape[0] == 0:
raise analysis_utils.IncompleteInputError('Meta data is empty. Stopping interpretation.')
self.interpreter.set_meta_data(self.meta_data) # tell interpreter the word index per readout to be able to calculate the event number per read out
meta_data_size = self.meta_data.shape[0]
self.meta_event_index = np.zeros((meta_data_size,), dtype=[('metaEventIndex', np.uint64)]) # this array is filled by the interpreter and holds the event number per read out
self.interpreter.set_meta_event_data(self.meta_event_index) # tell the interpreter the data container to write the meta event index to
if self.scan_parameters is None:
self.histogram.set_no_scan_parameter()
else:
self.scan_parameter_index = analysis_utils.get_scan_parameters_index(self.scan_parameters) # a array that labels unique scan parameter combinations
self.histogram.add_scan_parameter(self.scan_parameter_index) # just add an index for the different scan parameter combinations
if self._create_cluster_size_hist: # Cluster size result histogram
self._cluster_size_hist = np.zeros(shape=(6, ), dtype=np.uint32)
if self._create_cluster_tot_hist: # Cluster tot/size result histogram
self._cluster_tot_hist = np.zeros(shape=(16, 6), dtype=np.uint32)
close_analyzed_data_file = False
if analyzed_data_file is not None: # if an output file name is specified create new file for analyzed data
if self.is_open(self.out_file_h5) and os.path.abspath(analyzed_data_file) == os.path.abspath(self.out_file_h5.filename):
out_file_h5 = self.out_file_h5
else:
# normalize path
analyzed_data_file = os.path.abspath(analyzed_data_file)
if os.path.splitext(analyzed_data_file)[1].lower() != ".h5":
analyzed_data_file = os.path.splitext(analyzed_data_file)[0] + ".h5"
out_file_h5 = tb.open_file(analyzed_data_file, mode="w", title="Interpreted FE-I4 raw data")
close_analyzed_data_file = True
elif self.is_open(self.out_file_h5):
out_file_h5 = self.out_file_h5
else:
out_file_h5 = None
tmp_out_file_h5 = self.out_file_h5
if not self.is_open(self.out_file_h5) and self.is_open(out_file_h5):
close_analyzed_data_file = False
tmp_out_file_h5 = out_file_h5
self.out_file_h5 = out_file_h5
if self.is_open(self.out_file_h5):
self._analyzed_data_file = self.out_file_h5.filename
else:
self._analyzed_data_file is None
if self._analyzed_data_file is not None:
if self._create_hit_table is True:
description = data_struct.HitInfoTable().columns.copy()
hit_table = self.out_file_h5.create_table(self.out_file_h5.root, name='Hits', description=description, title='hit_data', filters=self._filter_table, chunkshape=(self._chunk_size / 100,))
if self._create_meta_word_index is True:
meta_word_index_table = self.out_file_h5.create_table(self.out_file_h5.root, name='EventMetaData', description=data_struct.MetaInfoWordTable, title='event_meta_data', filters=self._filter_table, chunkshape=(self._chunk_size / 10,))
if self._create_cluster_table:
cluster_table = self.out_file_h5.create_table(self.out_file_h5.root, name='Cluster', description=data_struct.ClusterInfoTable, title='Cluster data', filters=self._filter_table, expectedrows=self._chunk_size)
if self._create_cluster_hit_table:
description = data_struct.ClusterHitInfoTable().columns.copy()
cluster_hit_table = self.out_file_h5.create_table(self.out_file_h5.root, name='ClusterHits', description=description, title='cluster_hit_data', filters=self._filter_table, expectedrows=self._chunk_size)
logging.info("Interpreting raw data...")
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.AdaptiveETA()], maxval=analysis_utils.get_total_n_data_words(self.files_dict), term_width=80)
progress_bar.start()
total_words = 0
for file_index, raw_data_file in enumerate(self.files_dict.keys()): # loop over all raw data files
self.interpreter.reset_meta_data_counter()
with tb.open_file(raw_data_file, mode="r") as in_file_h5:
if use_settings_from_file:
self._deduce_settings_from_file(in_file_h5)
else:
self.fei4b = fei4b
if self.interpreter.meta_table_v2:
index_start = in_file_h5.root.meta_data.read(field='index_start')
index_stop = in_file_h5.root.meta_data.read(field='index_stop')
else:
index_start = in_file_h5.root.meta_data.read(field='start_index')
index_stop = in_file_h5.root.meta_data.read(field='stop_index')
bad_word_index = set()
# Check for bad data
if self._correct_corrupted_data:
tw = 2147483648 # trigger word
dh = 15269888 # data header
is_fe_data_header = logical_and(is_fe_word, is_data_header)
found_first_trigger = False
readout_slices = np.column_stack((index_start, index_stop))
previous_prepend_data_headers = None
prepend_data_headers = None
last_good_readout_index = None
last_index_with_event_data = None
for read_out_index, (index_start, index_stop) in enumerate(readout_slices):
try:
raw_data = in_file_h5.root.raw_data.read(index_start, index_stop)
except OverflowError, e:
pass
except tb.exceptions.HDF5ExtError:
break
# previous data chunk had bad data, check for good data
if (index_start - 1) in bad_word_index:
bad_data, current_prepend_data_headers, _ , _ = check_bad_data(raw_data, prepend_data_headers=1, trig_count=None)
if bad_data:
bad_word_index = bad_word_index.union(range(index_start, index_stop))
else:
# logging.info("found good data in %s from index %d to %d (chunk %d, length %d)" % (in_file_h5.filename, index_start, index_stop, read_out_index, (index_stop - index_start)))
if last_good_readout_index + 1 == read_out_index - 1:
logging.warning("found bad data in %s from index %d to %d (chunk %d, length %d)" % (in_file_h5.filename, readout_slices[last_good_readout_index][1], readout_slices[read_out_index - 1][1], last_good_readout_index + 1, (readout_slices[read_out_index - 1][1] - readout_slices[last_good_readout_index][1])))
else:
logging.warning("found bad data in %s from index %d to %d (chunk %d to %d, length %d)" % (in_file_h5.filename, readout_slices[last_good_readout_index][1], readout_slices[read_out_index - 1][1], last_good_readout_index + 1, read_out_index - 1, (readout_slices[read_out_index - 1][1] - readout_slices[last_good_readout_index][1])))
previous_good_raw_data = in_file_h5.root.raw_data.read(readout_slices[last_good_readout_index][0], readout_slices[last_good_readout_index][1] - 1)
previous_bad_raw_data = in_file_h5.root.raw_data.read(readout_slices[last_good_readout_index][1] - 1, readout_slices[read_out_index - 1][1])
fixed_raw_data, _ = fix_raw_data(previous_bad_raw_data, lsb_byte=None)
fixed_raw_data = np.r_[previous_good_raw_data, fixed_raw_data, raw_data]
_, prepend_data_headers, n_triggers, n_dh = check_bad_data(fixed_raw_data, prepend_data_headers=previous_prepend_data_headers, trig_count=self.trig_count)
last_good_readout_index = read_out_index
if n_triggers != 0 or n_dh != 0:
last_index_with_event_data = read_out_index
last_event_data_prepend_data_headers = prepend_data_headers
fixed_previous_raw_data = np.r_[previous_good_raw_data, fixed_raw_data]
_, previous_prepend_data_headers, _ , _ = check_bad_data(fixed_previous_raw_data, prepend_data_headers=previous_prepend_data_headers, trig_count=self.trig_count)
# check for bad data
else:
# workaround for first data chunk, might have missing trigger in some rare cases (already fixed in firmware)
if read_out_index == 0 and (np.any(is_trigger_word(raw_data) >= 1) or np.any(is_fe_data_header(raw_data) >= 1)):
bad_data, current_prepend_data_headers, n_triggers , n_dh = check_bad_data(raw_data, prepend_data_headers=1, trig_count=None)
# check for full last event in data
if current_prepend_data_headers == self.trig_count:
current_prepend_data_headers = None
# usually check for bad data happens here
else:
bad_data, current_prepend_data_headers, n_triggers , n_dh = check_bad_data(raw_data, prepend_data_headers=prepend_data_headers, trig_count=self.trig_count)
# do additional check with follow up data chunk and decide whether current chunk is defect or not
if bad_data:
if read_out_index == 0:
fixed_raw_data_chunk, _ = fix_raw_data(raw_data, lsb_byte=None)
fixed_raw_data_list = [fixed_raw_data_chunk]
else:
previous_raw_data = in_file_h5.root.raw_data.read(*readout_slices[read_out_index - 1])
raw_data_with_previous_data_word = np.r_[previous_raw_data[-1], raw_data]
fixed_raw_data_chunk, _ = fix_raw_data(raw_data_with_previous_data_word, lsb_byte=None)
fixed_raw_data = np.r_[previous_raw_data[:-1], fixed_raw_data_chunk]
# last data word of chunk before broken chunk migh be a trigger word or data header which cannot be recovered
fixed_raw_data_with_tw = np.r_[previous_raw_data[:-1], tw, fixed_raw_data_chunk]
fixed_raw_data_with_dh = np.r_[previous_raw_data[:-1], dh, fixed_raw_data_chunk]
fixed_raw_data_list = [fixed_raw_data, fixed_raw_data_with_tw, fixed_raw_data_with_dh]
bad_fixed_data, _, _ , _ = check_bad_data(fixed_raw_data_with_dh, prepend_data_headers=previous_prepend_data_headers, trig_count=self.trig_count)
bad_fixed_data = map(lambda data: check_bad_data(data, prepend_data_headers=previous_prepend_data_headers, trig_count=self.trig_count)[0], fixed_raw_data_list)
if not all(bad_fixed_data): # good fixed data
# last word in chunk before currrent chunk is also bad
if index_start != 0:
bad_word_index.add(index_start - 1)
# adding all word from current chunk
bad_word_index = bad_word_index.union(range(index_start, index_stop))
last_good_readout_index = read_out_index - 1
else:
# a previous chunk might be broken and the last data word becomes a trigger word, so do additional checks
if last_index_with_event_data and last_event_data_prepend_data_headers != read_out_index:
before_bad_raw_data = in_file_h5.root.raw_data.read(readout_slices[last_index_with_event_data - 1][0], readout_slices[last_index_with_event_data - 1][1] - 1)
previous_bad_raw_data = in_file_h5.root.raw_data.read(readout_slices[last_index_with_event_data][0] - 1, readout_slices[last_index_with_event_data][1])
fixed_raw_data, _ = fix_raw_data(previous_bad_raw_data, lsb_byte=None)
previous_good_raw_data = in_file_h5.root.raw_data.read(readout_slices[last_index_with_event_data][1], readout_slices[read_out_index - 1][1])
fixed_raw_data = np.r_[before_bad_raw_data, fixed_raw_data, previous_good_raw_data, raw_data]
bad_fixed_previous_data, current_prepend_data_headers, _, _ = check_bad_data(fixed_raw_data, prepend_data_headers=last_event_data_prepend_data_headers, trig_count=self.trig_count)
if not bad_fixed_previous_data:
logging.warning("found bad data in %s from index %d to %d (chunk %d, length %d)" % (in_file_h5.filename, readout_slices[last_index_with_event_data][0], readout_slices[last_index_with_event_data][1], last_index_with_event_data, (readout_slices[last_index_with_event_data][1] - readout_slices[last_index_with_event_data][0])))
bad_word_index = bad_word_index.union(range(readout_slices[last_index_with_event_data][0] - 1, readout_slices[last_index_with_event_data][1]))
else:
logging.warning("found bad data which cannot be corrected in %s from index %d to %d (chunk %d, length %d)" % (in_file_h5.filename, index_start, index_stop, read_out_index, (index_stop - index_start)))
else:
logging.warning("found bad data which cannot be corrected in %s from index %d to %d (chunk %d, length %d)" % (in_file_h5.filename, index_start, index_stop, read_out_index, (index_stop - index_start)))
if n_triggers != 0 or n_dh != 0:
last_index_with_event_data = read_out_index
last_event_data_prepend_data_headers = prepend_data_headers
if not bad_data or (bad_data and bad_fixed_data):
previous_prepend_data_headers = prepend_data_headers
prepend_data_headers = current_prepend_data_headers
consecutive_bad_words_list = consecutive(sorted(bad_word_index))
lsb_byte = None
# Loop over raw data in chunks
for word_index in range(0, in_file_h5.root.raw_data.shape[0], self._chunk_size): # loop over all words in the actual raw data file
try:
raw_data = in_file_h5.root.raw_data.read(word_index, word_index + self._chunk_size)
except OverflowError, e:
logging.error('%s: 2^31 xrange() limitation in 32-bit Python', e)
except tb.exceptions.HDF5ExtError:
logging.warning('Raw data file %s has missing raw data. Continue raw data analysis.', in_file_h5.filename)
break
total_words += raw_data.shape[0]
# fix bad data
if self._correct_corrupted_data:
# increase word shift for every bad data chunk in raw data chunk
word_shift = 0
chunk_indices = np.arange(word_index, word_index + self._chunk_size)
for consecutive_bad_word_indices in consecutive_bad_words_list:
selected_words = np.intersect1d(consecutive_bad_word_indices, chunk_indices, assume_unique=True)
if selected_words.shape[0]:
fixed_raw_data, lsb_byte = fix_raw_data(raw_data[selected_words - word_index - word_shift], lsb_byte=lsb_byte)
raw_data = np.r_[raw_data[:selected_words[0] - word_index - word_shift], fixed_raw_data, raw_data[selected_words[-1] - word_index + 1 - word_shift:]]
# check if last word of bad data chunk in current raw data chunk
if consecutive_bad_word_indices[-1] in selected_words:
lsb_byte = None
# word shift by removing data word at the beginning of each defect chunk
word_shift += 1
# bad data chunk is at the end of current raw data chunk
else:
break
self.interpreter.interpret_raw_data(raw_data) # interpret the raw data
# store remaining buffered event in the interpreter at the end of the last file
if file_index == len(self.files_dict.keys()) - 1 and word_index == range(0, in_file_h5.root.raw_data.shape[0], self._chunk_size)[-1]: # store hits of the latest event of the last file
self.interpreter.store_event()
hits = self.interpreter.get_hits()
if self.scan_parameters is not None:
nEventIndex = self.interpreter.get_n_meta_data_event()
self.histogram.add_meta_event_index(self.meta_event_index, nEventIndex)
if self.is_histogram_hits():
self.histogram_hits(hits)
if self.is_cluster_hits():
cluster_hits, clusters = self.cluster_hits(hits)
if self._create_cluster_hit_table:
cluster_hit_table.append(cluster_hits)
if self._create_cluster_table:
cluster_table.append(clusters)
if self._create_cluster_size_hist:
if clusters['size'].shape[0] > 0 and np.max(clusters['size']) + 1 > self._cluster_size_hist.shape[0]:
self._cluster_size_hist.resize(np.max(clusters['size']) + 1)
self._cluster_size_hist += fast_analysis_utils.hist_1d_index(clusters['size'], shape=self._cluster_size_hist.shape)
if self._create_cluster_tot_hist:
if clusters['tot'].shape[0] > 0 and np.max(clusters['tot']) + 1 > self._cluster_tot_hist.shape[0]:
self._cluster_tot_hist.resize((np.max(clusters['tot']) + 1, self._cluster_tot_hist.shape[1]))
if clusters['size'].shape[0] > 0 and np.max(clusters['size']) + 1 > self._cluster_tot_hist.shape[1]:
self._cluster_tot_hist.resize((self._cluster_tot_hist.shape[0], np.max(clusters['size']) + 1))
self._cluster_tot_hist += fast_analysis_utils.hist_2d_index(clusters['tot'], clusters['size'], shape=self._cluster_tot_hist.shape)
if self._analyzed_data_file is not None and self._create_hit_table:
hit_table.append(hits)
if self._analyzed_data_file is not None and self._create_meta_word_index:
size = self.interpreter.get_n_meta_data_word()
meta_word_index_table.append(meta_word[:size])
if total_words <= progress_bar.maxval: # Otherwise exception is thrown
progress_bar.update(total_words)
self.out_file_h5.flush()
progress_bar.finish()
self._create_additional_data()
if close_analyzed_data_file:
self.out_file_h5.close()
self.out_file_h5 = None
self.out_file_h5 = out_file_h5
if self.is_open(self.out_file_h5):
self._analyzed_data_file = self.out_file_h5.filename
else:
self._analyzed_data_file = None
|
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Interprets the raw data word table of all given raw data files with the c++ library.
Creates the h5 output file and PDF plots.
Parameters
----------
analyzed_data_file : string
The file name of the output analyzed data file. If None, the output analyzed data file
specified during initialization is taken.
use_settings_from_file : boolean
True if the needed parameters should be extracted from the raw data file
fei4b : boolean
True if the raw data is from FE-I4B.
|
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"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analyze_raw_data.py#L643-L921
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analyze_raw_data.py
|
AnalyzeRawData.analyze_hit_table
|
def analyze_hit_table(self, analyzed_data_file=None, analyzed_data_out_file=None):
'''Analyzes a hit table with the c++ histogrammming/clusterizer.
Parameters
----------
analyzed_data_file : string
The filename of the analyzed data file. If None, the analyzed data file
specified during initialization is taken.
Filename extension (.h5) does not need to be provided.
analyzed_data_out_file : string
The filename of the new analyzed data file. If None, the analyzed data file
specified during initialization is taken.
Filename extension (.h5) does not need to be provided.
'''
close_analyzed_data_file = False
if analyzed_data_file is not None: # if an output file name is specified create new file for analyzed data
if self.is_open(self.out_file_h5) and os.path.abspath(analyzed_data_file) == os.path.abspath(self.out_file_h5.filename):
in_file_h5 = self.out_file_h5
else:
# normalize path
analyzed_data_file = os.path.abspath(analyzed_data_file)
if os.path.splitext(analyzed_data_file)[1].lower() != ".h5":
analyzed_data_file = os.path.splitext(analyzed_data_file)[0] + ".h5"
in_file_h5 = tb.open_file(analyzed_data_file, mode="r+")
close_analyzed_data_file = True
elif self.is_open(self.out_file_h5):
in_file_h5 = self.out_file_h5
else:
raise ValueError('Parameter "analyzed_data_file" not specified.')
# set output file if an output file name is given, otherwise check if an output file is already opened
close_analyzed_data_out_file = False
if analyzed_data_out_file is not None: # if an output file name is specified create new file for analyzed data
if self.is_open(self.out_file_h5) and os.path.abspath(analyzed_data_out_file) == os.path.abspath(self.out_file_h5.filename):
out_file_h5 = self.out_file_h5
elif self.is_open(in_file_h5) and os.path.abspath(analyzed_data_out_file) == os.path.abspath(in_file_h5.filename):
out_file_h5 = in_file_h5
else:
# normalize path
analyzed_data_out_file = os.path.abspath(analyzed_data_out_file)
if os.path.splitext(analyzed_data_out_file)[1].lower() != ".h5":
analyzed_data_out_file = os.path.splitext(analyzed_data_out_file)[0] + ".h5"
out_file_h5 = tb.open_file(analyzed_data_out_file, mode="w", title="Analyzed FE-I4 hits")
close_analyzed_data_out_file = True
elif self.is_open(self.out_file_h5):
out_file_h5 = self.out_file_h5
else:
raise ValueError('Parameter "analyzed_data_out_file" not specified.')
tmp_out_file_h5 = self.out_file_h5
if not self.is_open(self.out_file_h5):
if os.path.abspath(in_file_h5.filename) == os.path.abspath(out_file_h5.filename):
close_analyzed_data_file = False
tmp_out_file_h5 = in_file_h5
self.out_file_h5 = out_file_h5
self._analyzed_data_file = self.out_file_h5.filename
if self._create_cluster_table:
cluster_table = self.out_file_h5.create_table(self.out_file_h5.root, name='Cluster', description=data_struct.ClusterInfoTable, title='cluster_hit_data', filters=self._filter_table, expectedrows=self._chunk_size)
if self._create_cluster_hit_table:
cluster_hit_table = self.out_file_h5.create_table(self.out_file_h5.root, name='ClusterHits', description=data_struct.ClusterHitInfoTable, title='cluster_hit_data', filters=self._filter_table, expectedrows=self._chunk_size)
if self._create_cluster_size_hist: # Cluster size result histogram
self._cluster_size_hist = np.zeros(shape=(6, ), dtype=np.uint32)
if self._create_cluster_tot_hist: # Cluster tot/size result histogram
self._cluster_tot_hist = np.zeros(shape=(16, 6), dtype=np.uint32)
try:
meta_data_table = in_file_h5.root.meta_data
meta_data = meta_data_table[:]
self.scan_parameters = analysis_utils.get_unique_scan_parameter_combinations(meta_data, scan_parameter_columns_only=True)
if self.scan_parameters is not None: # check if there is an additional column after the error code column, if yes this column has scan parameter infos
meta_event_index = np.ascontiguousarray(analysis_utils.get_unique_scan_parameter_combinations(meta_data)['event_number'].astype(np.uint64))
self.histogram.add_meta_event_index(meta_event_index, array_length=len(meta_event_index))
self.scan_parameter_index = analysis_utils.get_scan_parameters_index(self.scan_parameters) # a array that labels unique scan parameter combinations
self.histogram.add_scan_parameter(self.scan_parameter_index) # just add an index for the different scan parameter combinations
scan_parameter_names = analysis_utils.get_scan_parameter_names(self.scan_parameters)
logging.info('Adding scan parameter(s) for analysis: %s', (', ').join(scan_parameter_names) if scan_parameter_names else 'None',)
else:
logging.info("No scan parameter data provided")
self.histogram.set_no_scan_parameter()
except tb.exceptions.NoSuchNodeError:
logging.info("No meta data provided")
self.histogram.set_no_scan_parameter()
table_size = in_file_h5.root.Hits.nrows
n_hits = 0 # number of hits in actual chunk
logging.info('Analyzing hits...')
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.AdaptiveETA()], maxval=table_size, term_width=80)
progress_bar.start()
for hits, index in analysis_utils.data_aligned_at_events(in_file_h5.root.Hits, chunk_size=self._chunk_size):
n_hits += hits.shape[0]
if self.is_cluster_hits():
cluster_hits, clusters = self.cluster_hits(hits)
if self.is_histogram_hits():
self.histogram_hits(hits)
if self._analyzed_data_file is not None and self._create_cluster_hit_table:
cluster_hit_table.append(cluster_hits)
if self._analyzed_data_file is not None and self._create_cluster_table:
cluster_table.append(clusters)
if self._create_cluster_size_hist:
if clusters['size'].shape[0] > 0 and np.max(clusters['size']) + 1 > self._cluster_size_hist.shape[0]:
self._cluster_size_hist.resize(np.max(clusters['size']) + 1)
self._cluster_size_hist += fast_analysis_utils.hist_1d_index(clusters['size'], shape=self._cluster_size_hist.shape)
if self._create_cluster_tot_hist:
if clusters['tot'].shape[0] > 0 and np.max(clusters['tot']) + 1 > self._cluster_tot_hist.shape[0]:
self._cluster_tot_hist.resize((np.max(clusters['tot']) + 1, self._cluster_tot_hist.shape[1]))
if clusters['size'].shape[0] > 0 and np.max(clusters['size']) + 1 > self._cluster_tot_hist.shape[1]:
self._cluster_tot_hist.resize((self._cluster_tot_hist.shape[0], np.max(clusters['size']) + 1))
self._cluster_tot_hist += fast_analysis_utils.hist_2d_index(clusters['tot'], clusters['size'], shape=self._cluster_tot_hist.shape)
self.out_file_h5.flush()
progress_bar.update(index)
progress_bar.finish()
if table_size == 0:
logging.warning('Found no hits')
if n_hits != table_size:
raise analysis_utils.AnalysisError('Tables have different sizes. Not all hits were analyzed.')
self._create_additional_hit_data()
self._create_additional_cluster_data()
if close_analyzed_data_out_file:
out_file_h5.close()
if close_analyzed_data_file:
in_file_h5.close()
else:
self.out_file_h5 = tmp_out_file_h5
if self.is_open(self.out_file_h5):
self._analyzed_data_file = self.out_file_h5.filename
else:
self._analyzed_data_file = None
|
python
|
def analyze_hit_table(self, analyzed_data_file=None, analyzed_data_out_file=None):
'''Analyzes a hit table with the c++ histogrammming/clusterizer.
Parameters
----------
analyzed_data_file : string
The filename of the analyzed data file. If None, the analyzed data file
specified during initialization is taken.
Filename extension (.h5) does not need to be provided.
analyzed_data_out_file : string
The filename of the new analyzed data file. If None, the analyzed data file
specified during initialization is taken.
Filename extension (.h5) does not need to be provided.
'''
close_analyzed_data_file = False
if analyzed_data_file is not None: # if an output file name is specified create new file for analyzed data
if self.is_open(self.out_file_h5) and os.path.abspath(analyzed_data_file) == os.path.abspath(self.out_file_h5.filename):
in_file_h5 = self.out_file_h5
else:
# normalize path
analyzed_data_file = os.path.abspath(analyzed_data_file)
if os.path.splitext(analyzed_data_file)[1].lower() != ".h5":
analyzed_data_file = os.path.splitext(analyzed_data_file)[0] + ".h5"
in_file_h5 = tb.open_file(analyzed_data_file, mode="r+")
close_analyzed_data_file = True
elif self.is_open(self.out_file_h5):
in_file_h5 = self.out_file_h5
else:
raise ValueError('Parameter "analyzed_data_file" not specified.')
# set output file if an output file name is given, otherwise check if an output file is already opened
close_analyzed_data_out_file = False
if analyzed_data_out_file is not None: # if an output file name is specified create new file for analyzed data
if self.is_open(self.out_file_h5) and os.path.abspath(analyzed_data_out_file) == os.path.abspath(self.out_file_h5.filename):
out_file_h5 = self.out_file_h5
elif self.is_open(in_file_h5) and os.path.abspath(analyzed_data_out_file) == os.path.abspath(in_file_h5.filename):
out_file_h5 = in_file_h5
else:
# normalize path
analyzed_data_out_file = os.path.abspath(analyzed_data_out_file)
if os.path.splitext(analyzed_data_out_file)[1].lower() != ".h5":
analyzed_data_out_file = os.path.splitext(analyzed_data_out_file)[0] + ".h5"
out_file_h5 = tb.open_file(analyzed_data_out_file, mode="w", title="Analyzed FE-I4 hits")
close_analyzed_data_out_file = True
elif self.is_open(self.out_file_h5):
out_file_h5 = self.out_file_h5
else:
raise ValueError('Parameter "analyzed_data_out_file" not specified.')
tmp_out_file_h5 = self.out_file_h5
if not self.is_open(self.out_file_h5):
if os.path.abspath(in_file_h5.filename) == os.path.abspath(out_file_h5.filename):
close_analyzed_data_file = False
tmp_out_file_h5 = in_file_h5
self.out_file_h5 = out_file_h5
self._analyzed_data_file = self.out_file_h5.filename
if self._create_cluster_table:
cluster_table = self.out_file_h5.create_table(self.out_file_h5.root, name='Cluster', description=data_struct.ClusterInfoTable, title='cluster_hit_data', filters=self._filter_table, expectedrows=self._chunk_size)
if self._create_cluster_hit_table:
cluster_hit_table = self.out_file_h5.create_table(self.out_file_h5.root, name='ClusterHits', description=data_struct.ClusterHitInfoTable, title='cluster_hit_data', filters=self._filter_table, expectedrows=self._chunk_size)
if self._create_cluster_size_hist: # Cluster size result histogram
self._cluster_size_hist = np.zeros(shape=(6, ), dtype=np.uint32)
if self._create_cluster_tot_hist: # Cluster tot/size result histogram
self._cluster_tot_hist = np.zeros(shape=(16, 6), dtype=np.uint32)
try:
meta_data_table = in_file_h5.root.meta_data
meta_data = meta_data_table[:]
self.scan_parameters = analysis_utils.get_unique_scan_parameter_combinations(meta_data, scan_parameter_columns_only=True)
if self.scan_parameters is not None: # check if there is an additional column after the error code column, if yes this column has scan parameter infos
meta_event_index = np.ascontiguousarray(analysis_utils.get_unique_scan_parameter_combinations(meta_data)['event_number'].astype(np.uint64))
self.histogram.add_meta_event_index(meta_event_index, array_length=len(meta_event_index))
self.scan_parameter_index = analysis_utils.get_scan_parameters_index(self.scan_parameters) # a array that labels unique scan parameter combinations
self.histogram.add_scan_parameter(self.scan_parameter_index) # just add an index for the different scan parameter combinations
scan_parameter_names = analysis_utils.get_scan_parameter_names(self.scan_parameters)
logging.info('Adding scan parameter(s) for analysis: %s', (', ').join(scan_parameter_names) if scan_parameter_names else 'None',)
else:
logging.info("No scan parameter data provided")
self.histogram.set_no_scan_parameter()
except tb.exceptions.NoSuchNodeError:
logging.info("No meta data provided")
self.histogram.set_no_scan_parameter()
table_size = in_file_h5.root.Hits.nrows
n_hits = 0 # number of hits in actual chunk
logging.info('Analyzing hits...')
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.AdaptiveETA()], maxval=table_size, term_width=80)
progress_bar.start()
for hits, index in analysis_utils.data_aligned_at_events(in_file_h5.root.Hits, chunk_size=self._chunk_size):
n_hits += hits.shape[0]
if self.is_cluster_hits():
cluster_hits, clusters = self.cluster_hits(hits)
if self.is_histogram_hits():
self.histogram_hits(hits)
if self._analyzed_data_file is not None and self._create_cluster_hit_table:
cluster_hit_table.append(cluster_hits)
if self._analyzed_data_file is not None and self._create_cluster_table:
cluster_table.append(clusters)
if self._create_cluster_size_hist:
if clusters['size'].shape[0] > 0 and np.max(clusters['size']) + 1 > self._cluster_size_hist.shape[0]:
self._cluster_size_hist.resize(np.max(clusters['size']) + 1)
self._cluster_size_hist += fast_analysis_utils.hist_1d_index(clusters['size'], shape=self._cluster_size_hist.shape)
if self._create_cluster_tot_hist:
if clusters['tot'].shape[0] > 0 and np.max(clusters['tot']) + 1 > self._cluster_tot_hist.shape[0]:
self._cluster_tot_hist.resize((np.max(clusters['tot']) + 1, self._cluster_tot_hist.shape[1]))
if clusters['size'].shape[0] > 0 and np.max(clusters['size']) + 1 > self._cluster_tot_hist.shape[1]:
self._cluster_tot_hist.resize((self._cluster_tot_hist.shape[0], np.max(clusters['size']) + 1))
self._cluster_tot_hist += fast_analysis_utils.hist_2d_index(clusters['tot'], clusters['size'], shape=self._cluster_tot_hist.shape)
self.out_file_h5.flush()
progress_bar.update(index)
progress_bar.finish()
if table_size == 0:
logging.warning('Found no hits')
if n_hits != table_size:
raise analysis_utils.AnalysisError('Tables have different sizes. Not all hits were analyzed.')
self._create_additional_hit_data()
self._create_additional_cluster_data()
if close_analyzed_data_out_file:
out_file_h5.close()
if close_analyzed_data_file:
in_file_h5.close()
else:
self.out_file_h5 = tmp_out_file_h5
if self.is_open(self.out_file_h5):
self._analyzed_data_file = self.out_file_h5.filename
else:
self._analyzed_data_file = None
|
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Analyzes a hit table with the c++ histogrammming/clusterizer.
Parameters
----------
analyzed_data_file : string
The filename of the analyzed data file. If None, the analyzed data file
specified during initialization is taken.
Filename extension (.h5) does not need to be provided.
analyzed_data_out_file : string
The filename of the new analyzed data file. If None, the analyzed data file
specified during initialization is taken.
Filename extension (.h5) does not need to be provided.
|
[
"Analyzes",
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"clusterizer",
"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analyze_raw_data.py#L1067-L1204
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analyze_raw_data.py
|
AnalyzeRawData._deduce_settings_from_file
|
def _deduce_settings_from_file(self, opened_raw_data_file): # TODO: parse better
'''Tries to get the scan parameters needed for analysis from the raw data file
'''
try: # take infos raw data files (not avalable in old files)
flavor = opened_raw_data_file.root.configuration.miscellaneous[:][np.where(opened_raw_data_file.root.configuration.miscellaneous[:]['name'] == 'Flavor')]['value'][0]
self._settings_from_file_set = True
# adding this for special cases e.g., stop-mode scan
if "trig_count" in opened_raw_data_file.root.configuration.run_conf[:]['name']:
trig_count = opened_raw_data_file.root.configuration.run_conf[:][np.where(opened_raw_data_file.root.configuration.run_conf[:]['name'] == 'trig_count')]['value'][0]
else:
trig_count = opened_raw_data_file.root.configuration.global_register[:][np.where(opened_raw_data_file.root.configuration.global_register[:]['name'] == 'Trig_Count')]['value'][0]
vcal_c0 = opened_raw_data_file.root.configuration.calibration_parameters[:][np.where(opened_raw_data_file.root.configuration.calibration_parameters[:]['name'] == 'Vcal_Coeff_0')]['value'][0]
vcal_c1 = opened_raw_data_file.root.configuration.calibration_parameters[:][np.where(opened_raw_data_file.root.configuration.calibration_parameters[:]['name'] == 'Vcal_Coeff_1')]['value'][0]
c_low = opened_raw_data_file.root.configuration.calibration_parameters[:][np.where(opened_raw_data_file.root.configuration.calibration_parameters[:]['name'] == 'C_Inj_Low')]['value'][0]
c_mid = opened_raw_data_file.root.configuration.calibration_parameters[:][np.where(opened_raw_data_file.root.configuration.calibration_parameters[:]['name'] == 'C_Inj_Med')]['value'][0]
c_high = opened_raw_data_file.root.configuration.calibration_parameters[:][np.where(opened_raw_data_file.root.configuration.calibration_parameters[:]['name'] == 'C_Inj_High')]['value'][0]
self.c_low_mask = opened_raw_data_file.root.configuration.C_Low[:]
self.c_high_mask = opened_raw_data_file.root.configuration.C_High[:]
self.fei4b = False if str(flavor) == 'fei4a' else True
self.trig_count = int(trig_count)
self.vcal_c0 = float(vcal_c0)
self.vcal_c1 = float(vcal_c1)
self.c_low = float(c_low)
self.c_mid = float(c_mid)
self.c_high = float(c_high)
self.n_injections = int(opened_raw_data_file.root.configuration.run_conf[:][np.where(opened_raw_data_file.root.configuration.run_conf[:]['name'] == 'n_injections')]['value'][0])
except tb.exceptions.NoSuchNodeError:
if not self._settings_from_file_set:
logging.warning('No settings stored in raw data file %s, use standard settings', opened_raw_data_file.filename)
else:
logging.info('No settings provided in raw data file %s, use already set settings', opened_raw_data_file.filename)
except IndexError: # happens if setting is not available (e.g. repeat_command)
pass
|
python
|
def _deduce_settings_from_file(self, opened_raw_data_file): # TODO: parse better
'''Tries to get the scan parameters needed for analysis from the raw data file
'''
try: # take infos raw data files (not avalable in old files)
flavor = opened_raw_data_file.root.configuration.miscellaneous[:][np.where(opened_raw_data_file.root.configuration.miscellaneous[:]['name'] == 'Flavor')]['value'][0]
self._settings_from_file_set = True
# adding this for special cases e.g., stop-mode scan
if "trig_count" in opened_raw_data_file.root.configuration.run_conf[:]['name']:
trig_count = opened_raw_data_file.root.configuration.run_conf[:][np.where(opened_raw_data_file.root.configuration.run_conf[:]['name'] == 'trig_count')]['value'][0]
else:
trig_count = opened_raw_data_file.root.configuration.global_register[:][np.where(opened_raw_data_file.root.configuration.global_register[:]['name'] == 'Trig_Count')]['value'][0]
vcal_c0 = opened_raw_data_file.root.configuration.calibration_parameters[:][np.where(opened_raw_data_file.root.configuration.calibration_parameters[:]['name'] == 'Vcal_Coeff_0')]['value'][0]
vcal_c1 = opened_raw_data_file.root.configuration.calibration_parameters[:][np.where(opened_raw_data_file.root.configuration.calibration_parameters[:]['name'] == 'Vcal_Coeff_1')]['value'][0]
c_low = opened_raw_data_file.root.configuration.calibration_parameters[:][np.where(opened_raw_data_file.root.configuration.calibration_parameters[:]['name'] == 'C_Inj_Low')]['value'][0]
c_mid = opened_raw_data_file.root.configuration.calibration_parameters[:][np.where(opened_raw_data_file.root.configuration.calibration_parameters[:]['name'] == 'C_Inj_Med')]['value'][0]
c_high = opened_raw_data_file.root.configuration.calibration_parameters[:][np.where(opened_raw_data_file.root.configuration.calibration_parameters[:]['name'] == 'C_Inj_High')]['value'][0]
self.c_low_mask = opened_raw_data_file.root.configuration.C_Low[:]
self.c_high_mask = opened_raw_data_file.root.configuration.C_High[:]
self.fei4b = False if str(flavor) == 'fei4a' else True
self.trig_count = int(trig_count)
self.vcal_c0 = float(vcal_c0)
self.vcal_c1 = float(vcal_c1)
self.c_low = float(c_low)
self.c_mid = float(c_mid)
self.c_high = float(c_high)
self.n_injections = int(opened_raw_data_file.root.configuration.run_conf[:][np.where(opened_raw_data_file.root.configuration.run_conf[:]['name'] == 'n_injections')]['value'][0])
except tb.exceptions.NoSuchNodeError:
if not self._settings_from_file_set:
logging.warning('No settings stored in raw data file %s, use standard settings', opened_raw_data_file.filename)
else:
logging.info('No settings provided in raw data file %s, use already set settings', opened_raw_data_file.filename)
except IndexError: # happens if setting is not available (e.g. repeat_command)
pass
|
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] |
Tries to get the scan parameters needed for analysis from the raw data file
|
[
"Tries",
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"parameters",
"needed",
"for",
"analysis",
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] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analyze_raw_data.py#L1399-L1431
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analyze_raw_data.py
|
AnalyzeRawData._get_plsr_dac_charge
|
def _get_plsr_dac_charge(self, plsr_dac_array, no_offset=False):
'''Takes the PlsrDAC calibration and the stored C-high/C-low mask to calculate the charge from the PlsrDAC array on a pixel basis
'''
charge = np.zeros_like(self.c_low_mask, dtype=np.float16) # charge in electrons
if self.vcal_c0 is not None and self.vcal_c1 is not None and self.c_low is not None and self.c_mid is not None and self.c_high is not None:
voltage = self.vcal_c1 * plsr_dac_array if no_offset else self.vcal_c0 + self.vcal_c1 * plsr_dac_array
charge[np.logical_and(self.c_low_mask, ~self.c_high_mask)] = voltage[np.logical_and(self.c_low_mask, ~self.c_high_mask)] * self.c_low / 0.16022
charge[np.logical_and(~self.c_low_mask, self.c_high_mask)] = voltage[np.logical_and(self.c_low_mask, ~self.c_high_mask)] * self.c_mid / 0.16022
charge[np.logical_and(self.c_low_mask, self.c_high_mask)] = voltage[np.logical_and(self.c_low_mask, ~self.c_high_mask)] * self.c_high / 0.16022
return charge
|
python
|
def _get_plsr_dac_charge(self, plsr_dac_array, no_offset=False):
'''Takes the PlsrDAC calibration and the stored C-high/C-low mask to calculate the charge from the PlsrDAC array on a pixel basis
'''
charge = np.zeros_like(self.c_low_mask, dtype=np.float16) # charge in electrons
if self.vcal_c0 is not None and self.vcal_c1 is not None and self.c_low is not None and self.c_mid is not None and self.c_high is not None:
voltage = self.vcal_c1 * plsr_dac_array if no_offset else self.vcal_c0 + self.vcal_c1 * plsr_dac_array
charge[np.logical_and(self.c_low_mask, ~self.c_high_mask)] = voltage[np.logical_and(self.c_low_mask, ~self.c_high_mask)] * self.c_low / 0.16022
charge[np.logical_and(~self.c_low_mask, self.c_high_mask)] = voltage[np.logical_and(self.c_low_mask, ~self.c_high_mask)] * self.c_mid / 0.16022
charge[np.logical_and(self.c_low_mask, self.c_high_mask)] = voltage[np.logical_and(self.c_low_mask, ~self.c_high_mask)] * self.c_high / 0.16022
return charge
|
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"]",
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"self",
".",
"c_high",
"/",
"0.16022",
"return",
"charge"
] |
Takes the PlsrDAC calibration and the stored C-high/C-low mask to calculate the charge from the PlsrDAC array on a pixel basis
|
[
"Takes",
"the",
"PlsrDAC",
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"and",
"the",
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"C",
"-",
"high",
"/",
"C",
"-",
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"calculate",
"the",
"charge",
"from",
"the",
"PlsrDAC",
"array",
"on",
"a",
"pixel",
"basis"
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analyze_raw_data.py#L1433-L1442
|
yougov/solr-doc-manager
|
mongo_connector/doc_managers/solr_doc_manager.py
|
DocManager._parse_fields
|
def _parse_fields(self, result, field_name):
""" If Schema access, parse fields and build respective lists
"""
field_list = []
for key, value in result.get('schema', {}).get(field_name, {}).items():
if key not in field_list:
field_list.append(key)
return field_list
|
python
|
def _parse_fields(self, result, field_name):
""" If Schema access, parse fields and build respective lists
"""
field_list = []
for key, value in result.get('schema', {}).get(field_name, {}).items():
if key not in field_list:
field_list.append(key)
return field_list
|
[
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",",
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"field_list",
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",",
"{",
"}",
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"items",
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")",
":",
"if",
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"not",
"in",
"field_list",
":",
"field_list",
".",
"append",
"(",
"key",
")",
"return",
"field_list"
] |
If Schema access, parse fields and build respective lists
|
[
"If",
"Schema",
"access",
"parse",
"fields",
"and",
"build",
"respective",
"lists"
] |
train
|
https://github.com/yougov/solr-doc-manager/blob/1978bf6f3387b1afd6dd6b41a1bbaea9932d60fd/mongo_connector/doc_managers/solr_doc_manager.py#L83-L90
|
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