class PybarFEI4(Transceiver):
def setup_interpretation(self):
self.interpreter = PyDataInterpreter()
self.interpreter.set_warning_output(False)
def deserialze_data(self, data): # According to pyBAR data serilization
try:
self.meta_data = jsonapi.loads(data)
except ValueError:
try:
dtype = self.meta_data.pop('dtype')
shape = self.meta_data.pop('shape')
if self.meta_data:
try:
raw_data_array = np.frombuffer(
buffer(data), dtype=dtype).reshape(shape)
return raw_data_array
except (
KeyError, ValueError
): # KeyError happens if meta data read is omitted; ValueError if np.frombuffer fails due to wrong sha
return None
except AttributeError: # Happens if first data is not meta data
return None
return {'meta_data': self.meta_data}
def interpret_data(self, data):
if isinstance(
data[0][1],
dict): # Meta data is omitted, only raw data is interpreted
# Add info to meta data
data[0][1]['meta_data'].update({
'n_hits':
self.interpreter.get_n_hits(),
'n_events':
self.interpreter.get_n_events()
})
return [data[0][1]]
self.interpreter.interpret_raw_data(data[0][1])
interpreted_data = {
'hits':
self.interpreter.get_hits(),
'tdc_counters':
self.interpreter.get_tdc_counters(),
'error_counters':
self.interpreter.get_error_counters(),
'service_records_counters':
self.interpreter.get_service_records_counters(),
'trigger_error_counters':
self.interpreter.get_trigger_error_counters()
}
self.interpreter.reset_histograms(
) # For the summing of histograms the histogrammer converter is used
return [interpreted_data]
def serialze_data(self, data):
return jsonapi.dumps(data, cls=utils.NumpyEncoder)
class PybarFEI4(Transceiver):
def setup_interpretation(self):
self.interpreter = PyDataInterpreter()
self.interpreter.set_warning_output(False)
def deserialze_data(self, data): # According to pyBAR data serilization
try:
self.meta_data = jsonapi.loads(data)
except ValueError:
try:
dtype = self.meta_data.pop('dtype')
shape = self.meta_data.pop('shape')
if self.meta_data:
try:
raw_data_array = np.frombuffer(buffer(data), dtype=dtype).reshape(shape)
return raw_data_array
except (KeyError, ValueError): # KeyError happens if meta data read is omitted; ValueError if np.frombuffer fails due to wrong sha
return None
except AttributeError: # Happens if first data is not meta data
return None
return {'meta_data': self.meta_data}
def interpret_data(self, data):
if isinstance(data[0][1], dict): # Meta data is omitted, only raw data is interpreted
# Add info to meta data
data[0][1]['meta_data'].update({'n_hits': self.interpreter.get_n_hits(), 'n_events': self.interpreter.get_n_events()})
return [data[0][1]]
self.interpreter.interpret_raw_data(data[0][1])
interpreted_data = {
'hits': self.interpreter.get_hits(),
'tdc_counters': self.interpreter.get_tdc_counters(),
'error_counters': self.interpreter.get_error_counters(),
'service_records_counters': self.interpreter.get_service_records_counters(),
'trigger_error_counters': self.interpreter.get_trigger_error_counters()
}
self.interpreter.reset_histograms() # For the summing of histograms the histogrammer converter is used
return [interpreted_data]
def serialze_data(self, data):
return jsonapi.dumps(data, cls=utils.NumpyEncoder)
