def __init__(self, subarray, **kwargs):
"""
Parameters
----------
reducer_product : ctapipe.image.reducer.DataVolumeReducer
The DataVolumeReducer to use. If None, then
NullDataVolumeReducer will be used by default, and waveforms
will not be reduced.
extractor_product : ctapipe.image.extractor.ImageExtractor
The ImageExtractor to use. If None, then LocalPeakWindowSum
will be used by default.
calibration_path :
Path to LST calibration file to get the pedestal and flat-field corrections
kwargs
"""
super().__init__(subarray, **kwargs)
# load the waveform charge extractor
self.image_extractor = ImageExtractor.from_name(self.extractor_product,
subarray=self.subarray,
config=self.config)
self.log.info(f"extractor {self.extractor_product}")
print("EXTRACTOR", self.image_extractor)
self.data_volume_reducer = DataVolumeReducer.from_name(
self.reducer_product, subarray=self.subarray, config=self.config)
self.log.info(f" {self.reducer_product}")
# declare gain selector if the threshold is defined
if self.gain_threshold:
self.gain_selector = gainselection.ThresholdGainSelector(
threshold=self.gain_threshold)
# declare time calibrator if correction file exist
if os.path.exists(self.time_calibration_path):
self.time_corrector = PulseTimeCorrection(
calib_file_path=self.time_calibration_path)
else:
raise IOError(
f"Time calibration file {self.time_calibration_path} not found!"
)
# calibration data container
self.mon_data = MonitoringContainer()
# initialize the MonitoringContainer() for the moment it reads it from a hdf5 file
self._initialize_correction()
self.log.info(f"Global charge scale {self.charge_scale}")
def gain_selection(waveform, charges, pulse_time, threshold):
"""
Custom lst calibration.
Update event.dl1.tel[telescope_id] with calibrated image and peakpos
Parameters
----------
waveform: array of waveforms of the events
charges: array of calibrated pixel charges
pulse_time: array of pixel peak positions
cam_id: str
threshold: int threshold to change form high gain to low gain
"""
assert charges.shape[0] == 2
gain_selector = gainselection.ThresholdGainSelector(threshold=threshold)
waveform, gain_mask = gain_selector(waveform)
combined_image = charges[gain_mask, np.arange(charges.shape[1])]
combined_pulse_time = pulse_time[gain_mask, np.arange(pulse_time.shape[1])]
return combined_image, combined_pulse_time