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def | __init__ (self, *args, **kwargs) |
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def | runDataRef (self, dataRef, visitPairs) |
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def | fitCovariancesAstier (self, dataset, covariancesWithTagsArray) |
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def | getOutputPtcDataCovAstier (self, dataset, covFits) |
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def | measureMeanVarCov (self, exposure1, exposure2, region=None, covAstierRealSpace=False) |
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def | computeCovDirect (self, diffImage, weightImage, maxRange) |
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def | covDirectValue (self, diffImage, weightImage, dx, dy) |
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def | fitNonLinearityAndBuildLinearizers (self, datasetPtc, detector, tableArray=None, log=None) |
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def | fitNonLinearity (self, datasetPtc, tableArray=None) |
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def | calculateLinearityResidualAndLinearizers (self, exposureTimeVector, meanSignalVector) |
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def | buildLinearizerObject (self, datasetNonLinearity, detector, calibDate, linearizerType, instruName='', tableArray=None, log=None) |
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def | fitPtc (self, dataset, ptcFitType) |
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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 309 of file ptc.py.
def lsst.cp.pipe.ptc.MeasurePhotonTransferCurveTask.buildLinearizerObject |
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self, |
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datasetNonLinearity, |
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detector, |
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calibDate, |
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linearizerType, |
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instruName = '' , |
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tableArray = None , |
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log = None |
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Build linearizer object to persist.
Parameters
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datasetNonLinearity : `dict`
Dictionary of `lsst.cp.pipe.ptc.LinearityResidualsAndLinearizersDataset` objects.
detector : `lsst.afw.cameraGeom.Detector`
Detector object
calibDate : `datetime.datetime`
Calibration date
linearizerType : `str`
'LOOKUPTABLE', 'LINEARIZESQUARED', or 'LINEARIZEPOLYNOMIAL'
instruName : `str`, optional
Instrument name
tableArray : `np.array`, optional
Look-up table array with size rows=nAmps and columns=DN values
log : `lsst.log.Log`, optional
Logger to handle messages
Returns
-------
linearizer : `lsst.ip.isr.Linearizer`
Linearizer object
Definition at line 985 of file ptc.py.
def lsst.cp.pipe.ptc.MeasurePhotonTransferCurveTask.calculateLinearityResidualAndLinearizers |
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self, |
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exposureTimeVector, |
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meanSignalVector |
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Calculate linearity residual and fit an n-order polynomial to the mean vs time curve
to produce corrections (deviation from linear part of polynomial) for a particular amplifier
to populate LinearizeLookupTable.
Use the coefficients of this fit to calculate the correction coefficients for LinearizePolynomial
and LinearizeSquared."
Parameters
---------
exposureTimeVector: `list` of `float`
List of exposure times for each flat pair
meanSignalVector: `list` of `float`
List of mean signal from diference image of flat pairs
Returns
-------
dataset : `lsst.cp.pipe.ptc.LinearityResidualsAndLinearizersDataset`
The dataset containing the fit parameters, the NL correction coefficients, and the
LUT row for the amplifier at hand.
Notes
-----
datase members:
dataset.polynomialLinearizerCoefficients : `list` of `float`
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
(see `LinearizePolynomial` class in `linearize.py`.)
dataset.quadraticPolynomialLinearizerCoefficient : `float`
Coefficient for LinearizeSquared, where corrImage = uncorrImage + c0*uncorrImage^2.
c0 = -k2/(k1^2), where k1 and k2 are fit from
meanSignalVector = k0 + k1*exposureTimeVector + k2*exposureTimeVector^2 +...
+ kn*exposureTimeVector^n, with n = "polynomialFitDegreeNonLinearity".
dataset.linearizerTableRow : `list` of `float`
One dimensional array with deviation from linear part of n-order polynomial fit
to mean vs time curve. This array will be one row (for the particular amplifier at hand)
of the table array for LinearizeLookupTable.
dataset.meanSignalVsTimePolyFitPars : `list` of `float`
Parameters from n-order polynomial fit to meanSignalVector vs exposureTimeVector.
dataset.meanSignalVsTimePolyFitParsErr : `list` of `float`
Parameters from n-order polynomial fit to meanSignalVector vs exposureTimeVector.
dataset.meanSignalVsTimePolyFitReducedChiSq : `float`
Reduced unweighted chi squared from polynomial fit to meanSignalVector vs exposureTimeVector.
Definition at line 883 of file ptc.py.
def lsst.cp.pipe.ptc.MeasurePhotonTransferCurveTask.computeCovDirect |
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self, |
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diffImage, |
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weightImage, |
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maxRange |
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Compute covariances of diffImage in real space.
For lags larger than ~25, it is slower than the FFT way.
Taken from https://github.com/PierreAstier/bfptc/
Parameters
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diffImage : `numpy.array`
Image to compute the covariance of.
weightImage : `numpy.array`
Weight image of diffImage (1's and 0's for good and bad pixels, respectively).
maxRange : `int`
Last index of the covariance to be computed.
Returns
-------
outList : `list`
List with tuples of the form (dx, dy, var, cov, npix), where:
dx : `int`
Lag in x
dy : `int`
Lag in y
var : `float`
Variance at (dx, dy).
cov : `float`
Covariance at (dx, dy).
nPix : `int`
Number of pixel pairs used to evaluate var and cov.
Definition at line 689 of file ptc.py.
def lsst.cp.pipe.ptc.MeasurePhotonTransferCurveTask.covDirectValue |
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self, |
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diffImage, |
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weightImage, |
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dx, |
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dy |
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Compute covariances of diffImage in real space at lag (dx, dy).
Taken from https://github.com/PierreAstier/bfptc/ (c.f., appendix of Astier+19).
Parameters
----------
diffImage : `numpy.array`
Image to compute the covariance of.
weightImage : `numpy.array`
Weight image of diffImage (1's and 0's for good and bad pixels, respectively).
dx : `int`
Lag in x.
dy : `int`
Lag in y.
Returns
-------
cov : `float`
Covariance at (dx, dy)
nPix : `int`
Number of pixel pairs used to evaluate var and cov.
Definition at line 739 of file ptc.py.
def lsst.cp.pipe.ptc.MeasurePhotonTransferCurveTask.fitCovariancesAstier |
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self, |
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dataset, |
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covariancesWithTagsArray |
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Fit measured flat covariances to full model in Astier+19.
Parameters
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dataset : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
The dataset containing information such as the means, variances and exposure times.
covariancesWithTagsArray : `numpy.recarray`
Tuple with at least (mu, cov, var, i, j, npix), where:
mu : 0.5*(m1 + m2), where:
mu1: mean value of flat1
mu2: mean value of flat2
cov: covariance value at lag(i, j)
var: variance(covariance value at lag(0, 0))
i: lag dimension
j: lag dimension
npix: number of pixels used for covariance calculation.
Returns
-------
dataset: `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
This is the same dataset as the input paramter, however, it has been modified
to include information such as the fit vectors and the fit parameters. See
the class `PhotonTransferCurveDatase`.
Definition at line 497 of file ptc.py.
def lsst.cp.pipe.ptc.MeasurePhotonTransferCurveTask.fitNonLinearityAndBuildLinearizers |
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self, |
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datasetPtc, |
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detector, |
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tableArray = None , |
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log = None |
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Fit non-linearity function and build linearizer objects.
Parameters
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datasePtct : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
The dataset containing information such as the means, variances and exposure times.
nLinearity
detector : `lsst.afw.cameraGeom.Detector`
Detector object.
tableArray : `np.array`, optional
Optional. Look-up table array with size rows=nAmps and columns=DN values.
It will be modified in-place if supplied.
log : `lsst.log.Log`, optional
Logger to handle messages.
Returns
-------
linearizer : `lsst.ip.isr.Linearizer`
Linearizer object
Definition at line 795 of file ptc.py.
def lsst.cp.pipe.ptc.MeasurePhotonTransferCurveTask.fitPtc |
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self, |
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dataset, |
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ptcFitType |
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Fit the photon transfer curve to a polynimial or to Astier+19 approximation.
Fit the photon transfer curve with either a polynomial of the order
specified in the task config, or using the Astier approximation.
Sigma clipping is performed iteratively for the fit, as well as an
initial clipping of data points that are more than
config.initialNonLinearityExclusionThreshold away from lying on a
straight line. This other step is necessary because the photon transfer
curve turns over catastrophically at very high flux (because saturation
drops the variance to ~0) and these far outliers cause the initial fit
to fail, meaning the sigma cannot be calculated to perform the
sigma-clipping.
Parameters
----------
dataset : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
The dataset containing the means, variances and exposure times
ptcFitType : `str`
Fit a 'POLYNOMIAL' (degree: 'polynomialFitDegree') or
'EXPAPPROXIMATION' (Eq. 16 of Astier+19) to the PTC
Returns
-------
dataset: `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
This is the same dataset as the input paramter, however, it has been modified
to include information such as the fit vectors and the fit parameters. See
the class `PhotonTransferCurveDatase`.
Definition at line 1133 of file ptc.py.
def lsst.cp.pipe.ptc.MeasurePhotonTransferCurveTask.getOutputPtcDataCovAstier |
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self, |
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dataset, |
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covFits |
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Get output data for PhotonTransferCurveCovAstierDataset from CovFit objects.
Parameters
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dataset : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
The dataset containing information such as the means, variances and exposure times.
covFits: `dict`
Dictionary of CovFit objects, with amp names as keys.
Returns
-------
dataset : `lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
This is the same dataset as the input paramter, however, it has been modified
to include extra information such as the mask 1D array, gains, reoudout noise, measured signal,
measured variance, modeled variance, a, and b coefficient matrices (see Astier+19) per amplifier.
See the class `PhotonTransferCurveDatase`.
Definition at line 536 of file ptc.py.
def lsst.cp.pipe.ptc.MeasurePhotonTransferCurveTask.measureMeanVarCov |
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self, |
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exposure1, |
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exposure2, |
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region = None , |
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covAstierRealSpace = False |
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Calculate the mean of each of two exposures and the variance and covariance of their difference.
The variance is calculated via afwMath, and the covariance via the methods in Astier+19 (appendix A).
In theory, var = covariance[0,0]. This should be validated, and in the future, we may decide to just
keep one (covariance).
Parameters
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exposure1 : `lsst.afw.image.exposure.exposure.ExposureF`
First exposure of flat field pair.
exposure2 : `lsst.afw.image.exposure.exposure.ExposureF`
Second exposure of flat field pair.
region : `lsst.geom.Box2I`, optional
Region of each exposure where to perform the calculations (e.g, an amplifier).
covAstierRealSpace : `bool`, optional
Should the covariannces in Astier+19 be calculated in real space or via FFT?
See Appendix A of Astier+19.
Returns
-------
mu : `float` or `NaN`
0.5*(mu1 + mu2), where mu1, and mu2 are the clipped means of the regions in
both exposures. If either mu1 or m2 are NaN's, the returned value is NaN.
varDiff : `float` or `NaN`
Half of the clipped variance of the difference of the regions inthe two input
exposures. If either mu1 or m2 are NaN's, the returned value is NaN.
covDiffAstier : `list` or `NaN`
List with tuples of the form (dx, dy, var, cov, npix), where:
dx : `int`
Lag in x
dy : `int`
Lag in y
var : `float`
Variance at (dx, dy).
cov : `float`
Covariance at (dx, dy).
nPix : `int`
Number of pixel pairs used to evaluate var and cov.
If either mu1 or m2 are NaN's, the returned value is NaN.
Definition at line 574 of file ptc.py.