def read_datamodel(self):
h = RampModel(self.file)
#remove any non-pipeline related keywords (e.g. CV3 temps/voltages)
if 'extra_fits' in dir(h):
h.__delattr__('extra_fits')
self.data = h.data
#Currently a bug in level1bmodel when zeroframe is
#not present and a cube of zeros is returned
#If the datamodel returns a default zeroframe of all
#zeros, then set it to None here
#self.zeroframe = h.zeroframe
if np.all(h.zeroframe == 0):
print("Zeroframe in {}".format(self.file))
print("All zeros. Returning None.")
self.zeroframe = None
else:
self.zeroframe = h.zeroframe
self.sbAndRefpix = None
self.header = {}
for key in self.translate:
try:
self.header[key] = h.meta[self.translate[key]]
except:
self.header[key] = None
def read_datamodel(self):
logger = logging.getLogger('mirage.utils.read_fits.read_datamodel')
h = RampModel(self.file)
#remove any non-pipeline related keywords (e.g. CV3 temps/voltages)
if 'extra_fits' in dir(h):
h.__delattr__('extra_fits')
self.data = h.data
#Currently a bug in level1bmodel when zeroframe is
#not present and a cube of zeros is returned
#If the datamodel returns a default zeroframe of all
#zeros, then set it to None here
#self.zeroframe = h.zeroframe
if np.all(h.zeroframe == 0):
if 'RAPID' in h.meta.exposure.readpatt:
logger.info((
f"Zeroframe in {os.path.basename(self.file)} is all zeros. Since the readpattern is RAPID, "
"we grab a copy of the first group to be the zeroframe."))
self.zeroframe = h.data[:, 0, :, :]
else:
logger.info(
"Zeroframe in {} is all zeros. Returning None.".format(
self.file))
self.zeroframe = None
else:
self.zeroframe = h.zeroframe
self.sbAndRefpix = None
self.header = {}
for key in self.translate:
try:
self.header[key] = eval(f'h.meta.{self.translate[key]}')
except:
self.header[key] = None
