def __call__(self, gmos_raw_object):
"""
Preparing the Image
Parameters
----------
gmos_raw_object :
fname :
output filename
write_steps :
write out the individual steps to the individual fits files
write_cut_image :
fits file name to write out the cut_image to
"""
fits_data = gmos_raw_object.fits.fits_data
assert len(fits_data) - 1 == 3
# subtract overscan, get useful part of detector, correct for gain,
# and set read noise
if self.read_noise is None:
read_noise = [
detector.readout_noise
for detector in gmos_raw_object.instrument_setup.detectors
]
else:
read_noise = self.read_noise
if self.gain is None:
gain = [
detector.gain
for detector in gmos_raw_object.instrument_setup.detectors
]
else:
gain = self.gain
fits_file = prepare_frame.prepare(
fits_data,
bias_subslice=self.bias_subslice,
data_subslice=self.data_subslice,
clip=self.bias_clip_sigma,
gain=gain,
read_noise=read_noise,
combine=self.combine,
overscan_std_threshold=self.overscan_std_threshold)
# give each extension a name. May later add error/mask extensions
for i, extension in enumerate(fits_file):
if i > 0:
extension.name = 'chip{0:d}.data'.format(i)
return fits_file
def __call__(self, gmos_raw_object):
"""
Preparing the Image
Parameters
----------
gmos_raw_object :
fname :
output filename
write_steps :
write out the individual steps to the individual fits files
write_cut_image :
fits file name to write out the cut_image to
"""
fits_data = gmos_raw_object.fits.fits_data
assert len(fits_data) - 1 == 3
# subtract overscan, get useful part of detector, correct for gain,
# and set read noise
if self.read_noise is None:
read_noise = [detector.readout_noise for detector in
gmos_raw_object.instrument_setup.detectors]
else:
read_noise = self.read_noise
if self.gain is None:
gain = [detector.gain for detector in
gmos_raw_object.instrument_setup.detectors]
else:
gain = self.gain
fits_file = prepare_frame.prepare(fits_data,
bias_subslice=self.bias_subslice,
data_subslice=self.data_subslice,
clip=self.bias_clip_sigma,
gain=gain, read_noise=read_noise, combine=self.combine,
overscan_std_threshold=self.overscan_std_threshold)
# give each extension a name. May later add error/mask extensions
for i, extension in enumerate(fits_file):
if i > 0:
extension.name = 'chip{0:d}.data'.format(i)
return fits_file
def reduce(self):
prepared_frame = prepare_frame.prepare(self.fits.fits_data)
return qmosaic.qmosaic(*prepared_frame[1:4])
