def build_rn2img(self):
"""
Generate the model read noise squared image
Currently only used by ScienceImage.
Wrapper to procimg.rn_frame
Returns:
`numpy.ndarray`_: Copy of the read noise squared image
"""
# Check if we need to determine the read noise directly from the overscan region from any amplifier
numamps = len(self.ronoise)
for amp in range(numamps):
if self.ronoise[amp] <= 0.0:
biaspix = self.rawimage[self.oscansec_img == amp +
1] * self.detector['gain'][amp]
_, _, stddev = stats.sigma_clipped_stats(biaspix, sigma=5)
self.ronoise[amp] = stddev
msgs.info("Read noise of amplifier {0:d} = {1:.3f} e-".format(
amp + 1, self.ronoise[amp]))
# Build it
self.rn2img = procimg.rn_frame(self.datasec_img, self.detector['gain'],
self.ronoise)
# Return
return self.rn2img.copy()
def build_rn2img(self, trim=True):
"""
Generate the model read noise squared image
Currently only used by ScienceImage.
Wrapper to procimg.rn_frame
Returns
-------
self.rn2img : ndarray
"""
msgs.info(
"Generating read noise image from detector properties and amplifier layout)"
)
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.rn2img = procimg.rn_frame(datasec_img, detector['gain'],
detector['ronoise'])
self.steps.append(inspect.stack()[0][3])
# Return
return self.rn2img
def build_rn2img(self):
"""
Generate the model read noise squared image
Currently only used by ScienceImage.
Wrapper to procimg.rn_frame
Returns:
np.ndarray: Copy of the read noise squared image
"""
msgs.info(
"Generating read noise image from detector properties and amplifier layout)"
)
# Convenience
detector = self.spectrograph.detector[self.det - 1]
datasec_img = self.spectrograph.get_datasec_img(filename=self.filename,
det=self.det)
# Build it
self.rn2img = procimg.rn_frame(datasec_img,
detector['gain'],
detector['ronoise'],
numamplifiers=detector['numamplifiers'])
# Return
return self.rn2img.copy()
def build_rn2img(self):
"""
Generate the model read noise squared image
Currently only used by ScienceImage.
Wrapper to procimg.rn_frame
Returns:
`numpy.ndarray`_: Copy of the read noise squared image
"""
# Build it
self.rn2img = procimg.rn_frame(self.datasec_img, self.detector['gain'],
self.detector['ronoise'])
# Return
return self.rn2img.copy()