def build_rawvarframe(self, trim=True):
"""
Generate the Raw Variance frame
Currently only used by ScienceImage.
Wrapper to procimg.variance_frame
Returns
-------
self.rawvarframe : ndarray
"""
msgs.info(
"Generating raw variance frame (from detected counts [flat fielded])"
)
datasec_img = self.spectrograph.get_datasec_img(self.files[0],
det=self.det)
if trim:
datasec_img = procimg.trim_frame(datasec_img, datasec_img < 1)
detector = self.spectrograph.detector[self.det - 1]
self.rawvarframe = procimg.variance_frame(
datasec_img,
self.stack,
detector['gain'],
detector['ronoise'],
darkcurr=detector['darkcurr'],
exptime=self.exptime)
# Step
self.steps.append(inspect.stack()[0][3])
# Return
return self.rawvarframe
def build_ivar(self):
"""
Generate the Inverse Variance frame
Uses procimg.variance_frame
Returns:
np.ndarray: Copy of self.ivar
"""
msgs.info(
"Generating raw variance frame (from detected counts [flat fielded])"
)
# Convenience
detector = self.spectrograph.detector[self.det - 1]
# Generate
rawvarframe = procimg.variance_frame(
self.datasec_img,
self.image,
detector['gain'],
detector['ronoise'],
numamplifiers=detector['numamplifiers'],
darkcurr=detector['darkcurr'],
exptime=self.exptime,
rnoise=self.rn2img)
# Ivar
self.ivar = utils.inverse(rawvarframe)
# Return
return self.ivar.copy()
def build_ivar(self):
"""
Generate the Inverse Variance frame
Uses procimg.variance_frame
Returns:
`numpy.ndarray`_: Copy of self.ivar
"""
# Generate
rawvarframe = procimg.variance_frame(
self.datasec_img,
self.image,
self.detector['gain'],
self.detector['ronoise'],
darkcurr=self.detector['darkcurr'],
exptime=self.exptime,
rnoise=self.rn2img)
# Ivar
self.ivar = utils.inverse(rawvarframe)
# Return
return self.ivar.copy()
