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()