A class to calculate, fit, and plot a PTC from a set of flat pairs.
The Photon Transfer Curve (var(signal) vs mean(signal)) is a standard
tool used in astronomical detectors characterization (e.g., Janesick 2001,
Janesick 2007). If ptcFitType is "EXPAPPROXIMATION" or "POLYNOMIAL",
this task calculates the PTC from a series of pairs of flat-field images;
each pair taken at identical exposure times. The difference image of each
pair is formed to eliminate fixed pattern noise, and then the variance
of the difference image and the mean of the average image
are used to produce the PTC. An n-degree polynomial or the approximation
in Equation 16 of Astier+19 ("The Shape of the Photon Transfer Curve
of CCD sensors", arXiv:1905.08677) can be fitted to the PTC curve. These
models include parameters such as the gain (e/DN) and readout noise.
Linearizers to correct for signal-chain non-linearity are also calculated.
The `Linearizer` class, in general, can support per-amp linearizers, but
in this task this is not supported.
If ptcFitType is "FULLCOVARIANCE", the covariances of the difference
images are calculated via the DFT methods described in Astier+19 and the
variances for the PTC are given by the cov[0,0] elements at each signal
level. The full model in Equation 20 of Astier+19 is fit to the PTC
to get the gain and the noise.
Parameters
----------
*args: `list`
Positional arguments passed to the Task constructor. None used at this
time.
**kwargs: `dict`
Keyword arguments passed on to the Task constructor. None used at this
time.
Definition at line 64 of file measurePtcGen2Task.py.