DM-24703: Make linearity a subclass of lsst.ip.isr.IsrCalib #48
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| "LookupTable": "Create a lookup table solution.", | ||
| "Polynomial": "Create an arbitrary polynomial solution.", | ||
| "Squared": "Create a single order squared solution.", | ||
| "None": "Create a dummy solution.", |
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Craig has recently been obtaining results that indicate that we might also need to implement a spline fit option. Could this be something added to this ticket, or perhaps something for another ticket?
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I've added DM-26545 for this.
python/lsst/cp/pipe/linearity.py
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| def debugFit(self, stepname, timeVector, meanVector, linearizer, ampName): | ||
| """ |
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No docstring here? I guess up in L197 you are calling this function to produce linearity plots for debugging while the code is running, right? (like in the crosstalk code)
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That's the plan. Since this can be run independent of the previous steps (ISR, PTC), the results can be checked through the debug code. I've added the missing docstring.
| @@ -85,27 +84,17 @@ class MeasurePhotonTransferCurveTaskConfig(pexConfig.Config): | |||
| doc="Degree of polynomial to fit the PTC, when 'ptcFitType'=POLYNOMIAL.", | |||
| default=3, | |||
| ) | |||
| linearity = pexConfig.ConfigurableField( | |||
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I see that this is used below when calling this new linearity subtask, but I don't quite understand why this is a configuration field. (why is it here at this point?). I'm probably missing something.
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By making this a ConfigurableField, the target task can be instantiated as a subtask (L334). This allows the PTC code to call the subtask's run method directly (L448). This moves the linearity configuration to config.linearity.polynomialOrderand similar fields, instead of being directly part of the MeasurePhotonTransferCurveTaskConfig.
python/lsst/cp/pipe/ptc.py
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| # @dataclass | ||
| # class LinearityResidualsAndLinearizersDataset: | ||
| # """A simple class to hold the output from the | ||
| # `calculateLinearityResidualAndLinearizers` function. | ||
| # """ | ||
| # # Normalized coefficients for polynomial NL correction | ||
| # polynomialLinearizerCoefficients: list | ||
| # # Normalized coefficient for quadratic polynomial NL correction (c0) | ||
| # quadraticPolynomialLinearizerCoefficient: float | ||
| # # LUT array row for the amplifier at hand | ||
| # linearizerTableRow: list | ||
| # meanSignalVsTimePolyFitPars: list | ||
| # meanSignalVsTimePolyFitParsErr: list | ||
| # meanSignalVsTimePolyFitReducedChiSq: float |
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I see that this dataclass is fully commented. If it is not being used anymore, then it should be completely removed.
| Coefficients for LinearizePolynomial, where corrImage = uncorrImage + sum_i c_i uncorrImage^(2 + | ||
| i). | ||
| c_(j-2) = -k_j/(k_1^j) with units DN^(1-j) (c.f., Eq. 37 of 2003.05978). The units of k_j are | ||
| DN/t^j, and they are fit from meanSignalVector = k0 + k1*exposureTimeVector + | ||
| k2*exposureTimeVector^2 + ... + kn*exposureTimeVector^n, with | ||
| n = "polynomialFitDegreeNonLinearity". k_0 and k_1 and degenerate with bias level and gain, | ||
| and are not used by the non-linearity correction. Therefore, j = 2...n in the above expression |
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It seems that this explanation and reference got lost, but I think it helped the user understand a bit better how the polynomial correction was constructed. Could this (or a summary) be placed back somewhere? Or maybe we could still document this in the comments of the Jira ticket, at least.
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Added and updated this discussion in the Notes section of the run method docstring.
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PTC now calls the linearity code as a subtask. This updates the linearizer to be an IsrCalib. Adds gen3 handling for linearity. Use configurable test accuracy for linearity.
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Move linearity calibration construction out of
ptc.py, and make the new task gen3 compliant.