lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset Class Reference

Inheritance diagram for lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset:

Public Member Functions
	__init__ (self, ampNames=[], ptcFitType=None, covMatrixSide=1, covMatrixSideFullCovFit=None, **kwargs)
	setAmpValuesPartialDataset (self, ampName, inputExpIdPair=(-1, -1), rawExpTime=np.nan, rawMean=np.nan, rawVar=np.nan, rowMeanVariance=np.nan, photoCharge=np.nan, expIdMask=False, covariance=None, covSqrtWeights=None, gain=np.nan, noise=np.nan, histVar=np.nan, histChi2Dof=np.nan, kspValue=0.0, auxValues=None)
	setAuxValuesPartialDataset (self, auxDict)
	updateMetadata (self, **kwargs)
	fromDict (cls, dictionary)
	toDict (self)
	fromTable (cls, tableList)
	toTable (self)
	fromDetector (self, detector)
	getExpIdsUsed (self, ampName)
	getGoodAmps (self)
	getGoodPoints (self, ampName)
	validateGainNoiseTurnoffValues (self, ampName, doWarn=False)

Public Attributes
	ptcFitType
	ampNames
	covMatrixSide
	covMatrixSideFullCovFit
	badAmps
	inputExpIdPairs
	expIdMask
	rawExpTimes
	rawMeans
	rawVars
	rowMeanVariance
	photoCharges
	gain
	gainErr
	noiseList
	noise
	noiseErr
	histVars
	histChi2Dofs
	kspValues
	ptcFitPars
	ptcFitParsError
	ptcFitChiSq
	ptcTurnoff
	covariances
	covariancesModel
	covariancesSqrtWeights
	aMatrix
	bMatrix
	noiseMatrix
	covariancesModelNoB
	aMatrixNoB
	noiseMatrixNoB
	finalVars
	finalModelVars
	finalMeans
	auxValues

Protected Member Functions
	_validateCovarianceMatrizSizes (self)

Static Protected Attributes
str	_OBSTYPE = 'PTC'
str	_SCHEMA = 'Gen3 Photon Transfer Curve'
float	_VERSION = 1.7

Detailed Description

A simple class to hold the output data from the PTC task.

The dataset is made up of a dictionary for each item, keyed by the
amplifiers' names, which much be supplied at construction time.
New items cannot be added to the class to save accidentally saving to the
wrong property, and the class can be frozen if desired.
inputExpIdPairs records the exposures used to produce the data.
When fitPtc() or fitCovariancesAstier() is run, a mask is built up, which
is by definition always the same length as inputExpIdPairs, rawExpTimes,
rawMeans and rawVars, and is a list of bools, which are incrementally set
to False as points are discarded from the fits.
PTC fit parameters for polynomials are stored in a list in ascending order
of polynomial term, i.e. par[0]*x^0 + par[1]*x + par[2]*x^2 etc
with the length of the list corresponding to the order of the polynomial
plus one.

Parameters
----------
ampNames : `list`
    List with the names of the amplifiers of the detector at hand.
ptcFitType : `str`, optional
    Type of model fitted to the PTC: "POLYNOMIAL", "EXPAPPROXIMATION",
    or "FULLCOVARIANCE".
covMatrixSide : `int`, optional
    Maximum lag of measured covariances (size of square covariance
    matrices).
covMatrixSideFullCovFit : `int`, optional
    Maximum covariances lag for FULLCOVARIANCE fit. It should be less or
    equal than covMatrixSide.
kwargs : `dict`, optional
    Other keyword arguments to pass to the parent init.

Notes
-----
The stored attributes are:

badAmps : `list` [`str`]
    List with bad amplifiers names.
inputExpIdPairs : `dict`, [`str`, `list`]
    Dictionary keyed by amp names containing the input exposures IDs.
expIdMask : `dict`, [`str`, `np.ndarray`]
    Dictionary keyed by amp names containing the mask produced after
    outlier rejection. The mask produced by the "FULLCOVARIANCE"
    option may differ from the one produced in the other two PTC
    fit types.
rawExpTimes : `dict`, [`str`, `np.ndarray`]
    Dictionary keyed by amp names containing the unmasked exposure times.
rawMeans : `dict`, [`str`, `np.ndarray`]
    Dictionary keyed by amp names containing the unmasked average of the
    means of the exposures in each flat pair.
rawVars : `dict`, [`str`, `np.ndarray`]
    Dictionary keyed by amp names containing the variance of the
    difference image of the exposures in each flat pair.
rowMeanVariance : `dict`, [`str`, `np.ndarray`]
    Dictionary keyed by amp names containing the variance of the
    means of the rows of the difference image of the exposures
    in each flat pair.
histVars : `dict`, [`str`, `np.ndarray`]
    Dictionary keyed by amp names containing the variance of the
    difference image of the exposures in each flat pair estimated
    by fitting a Gaussian model.
histChi2Dofs : `dict`, [`str`, `np.ndarray`]
    Dictionary keyed by amp names containing the chi-squared per degree
    of freedom fitting the difference image to a Gaussian model.
kspValues : `dict`, [`str`, `np.ndarray`]
    Dictionary keyed by amp names containing the KS test p-value from
    fitting the difference image to a Gaussian model.
gain : `dict`, [`str`, `float`]
    Dictionary keyed by amp names containing the fitted gains.
gainErr : `dict`, [`str`, `float`]
    Dictionary keyed by amp names containing the errors on the
    fitted gains.
noiseList : `dict`, [`str`, `np.ndarray`]
    Dictionary keyed by amp names containing the mean read noise from
    each flat pair (as measured from overscan).
noise : `dict`, [`str`, `float`]
    Dictionary keyed by amp names containing the fitted noise.
noiseErr : `dict`, [`str`, `float`]
    Dictionary keyed by amp names containing the errors on the fitted
    noise.
ptcFitPars : `dict`, [`str`, `np.ndarray`]
    Dictionary keyed by amp names containing the fitted parameters of the
    PTC model for ptcFitTye in ["POLYNOMIAL", "EXPAPPROXIMATION"].
ptcFitParsError : `dict`, [`str`, `np.ndarray`]
    Dictionary keyed by amp names containing the errors on the fitted
    parameters of the PTC model for ptcFitTye in
    ["POLYNOMIAL", "EXPAPPROXIMATION"].
ptcFitChiSq : `dict`, [`str`, `float`]
    Dictionary keyed by amp names containing the reduced chi squared
    of the fit for ptcFitTye in ["POLYNOMIAL", "EXPAPPROXIMATION"].
ptcTurnoff : `dict` [`str, `float`]
    Flux value (in ADU) where the variance of the PTC curve starts
    decreasing consistently.
covariances : `dict`, [`str`, `np.ndarray`]
    Dictionary keyed by amp names containing a list of measured
    covariances per mean flux.
covariancesModel : `dict`, [`str`, `np.ndarray`]
    Dictionary keyed by amp names containinging covariances model
    (Eq. 20 of Astier+19) per mean flux.
covariancesSqrtWeights : `dict`, [`str`, `np.ndarray`]
    Dictionary keyed by amp names containinging sqrt. of covariances
    weights.
aMatrix : `dict`, [`str`, `np.ndarray`]
    Dictionary keyed by amp names containing the "a" parameters from
    the model in Eq. 20 of Astier+19.
bMatrix : `dict`, [`str`, `np.ndarray`]
    Dictionary keyed by amp names containing the "b" parameters from
    the model in Eq. 20 of Astier+19.
noiseMatrix : `dict`, [`str`, `np.ndarray`]
    Dictionary keyed by amp names containing the "noise" parameters from
    the model in Eq. 20 of Astier+19.
covariancesModelNoB : `dict`, [`str`, `np.ndarray`]
    Dictionary keyed by amp names containing covariances model
    (with 'b'=0 in Eq. 20 of Astier+19)
    per mean flux.
aMatrixNoB : `dict`, [`str`, `np.ndarray`]
    Dictionary keyed by amp names containing the "a" parameters from the
    model in Eq. 20 of Astier+19
    (and 'b' = 0).
noiseMatrixNoB : `dict`, [`str`, `np.ndarray`]
    Dictionary keyed by amp names containing the "noise" parameters from
    the model in Eq. 20 of Astier+19, with 'b' = 0.
finalVars : `dict`, [`str`, `np.ndarray`]
    Dictionary keyed by amp names containing the masked variance of the
    difference image of each flat
    pair. If needed, each array will be right-padded with
    np.nan to match the length of rawExpTimes.
finalModelVars : `dict`, [`str`, `np.ndarray`]
    Dictionary keyed by amp names containing the masked modeled
    variance of the difference image of each flat pair. If needed, each
    array will be right-padded with np.nan to match the length of
    rawExpTimes.
finalMeans : `dict`, [`str`, `np.ndarray`]
    Dictionary keyed by amp names containing the masked average of the
    means of the exposures in each flat pair. If needed, each array
    will be right-padded with np.nan to match the length of
    rawExpTimes.
photoCharges : `dict`, [`str`, `np.ndarray`]
    Dictionary keyed by amp names containing the integrated photocharge
    for linearity calibration.
auxValues : `dict`, [`str`, `np.ndarray`]
    Dictionary of per-detector auxiliary header values that can be used
    for PTC, linearity computation.

Version 1.1 adds the `ptcTurnoff` attribute.
Version 1.2 adds the `histVars`, `histChi2Dofs`, and `kspValues`
attributes.
Version 1.3 adds the `noiseMatrix` and `noiseMatrixNoB` attributes.
Version 1.4 adds the `auxValues` attribute.
Version 1.5 adds the `covMatrixSideFullCovFit` attribute.
Version 1.6 adds the `rowMeanVariance` attribute.
Version 1.7 adds the `noiseList` attribute.

Definition at line 35 of file ptcDataset.py.

Constructor & Destructor Documentation

__init__()

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.__init__	(		self,
			ampNames = [],
			ptcFitType = None,
			covMatrixSide = 1,
			covMatrixSideFullCovFit = None,
		**	kwargs )

Reimplemented from lsst.ip.isr.calibType.IsrCalib.

Definition at line 194 of file ptcDataset.py.

                 covMatrixSideFullCovFit=None, **kwargs):
        self.ptcFitType = ptcFitType
        self.ampNames = ampNames
        self.covMatrixSide = covMatrixSide
        if covMatrixSideFullCovFit is None:
            self.covMatrixSideFullCovFit = covMatrixSide
        else:
            self.covMatrixSideFullCovFit = covMatrixSideFullCovFit
 
        self.badAmps = []
 
        self.inputExpIdPairs = {ampName: [] for ampName in ampNames}
        self.expIdMask = {ampName: np.array([], dtype=bool) for ampName in ampNames}
        self.rawExpTimes = {ampName: np.array([]) for ampName in ampNames}
        self.rawMeans = {ampName: np.array([]) for ampName in ampNames}
        self.rawVars = {ampName: np.array([]) for ampName in ampNames}
        self.rowMeanVariance = {ampName: np.array([]) for ampName in ampNames}
        self.photoCharges = {ampName: np.array([]) for ampName in ampNames}
 
        self.gain = {ampName: np.nan for ampName in ampNames}
        self.gainErr = {ampName: np.nan for ampName in ampNames}
        self.noiseList = {ampName: np.array([]) for ampName in ampNames}
        self.noise = {ampName: np.nan for ampName in ampNames}
        self.noiseErr = {ampName: np.nan for ampName in ampNames}
 
        self.histVars = {ampName: np.array([]) for ampName in ampNames}
        self.histChi2Dofs = {ampName: np.array([]) for ampName in ampNames}
        self.kspValues = {ampName: np.array([]) for ampName in ampNames}
 
        self.ptcFitPars = {ampName: np.array([]) for ampName in ampNames}
        self.ptcFitParsError = {ampName: np.array([]) for ampName in ampNames}
        self.ptcFitChiSq = {ampName: np.nan for ampName in ampNames}
        self.ptcTurnoff = {ampName: np.nan for ampName in ampNames}
 
        self.covariances = {ampName: np.array([]) for ampName in ampNames}
        self.covariancesModel = {ampName: np.array([]) for ampName in ampNames}
        self.covariancesSqrtWeights = {ampName: np.array([]) for ampName in ampNames}
        self.aMatrix = {ampName: np.array([]) for ampName in ampNames}
        self.bMatrix = {ampName: np.array([]) for ampName in ampNames}
        self.noiseMatrix = {ampName: np.array([]) for ampName in ampNames}
        self.covariancesModelNoB = {ampName: np.array([]) for ampName in ampNames}
        self.aMatrixNoB = {ampName: np.array([]) for ampName in ampNames}
        self.noiseMatrixNoB = {ampName: np.array([]) for ampName in ampNames}
 
        self.finalVars = {ampName: np.array([]) for ampName in ampNames}
        self.finalModelVars = {ampName: np.array([]) for ampName in ampNames}
        self.finalMeans = {ampName: np.array([]) for ampName in ampNames}
 
        # Try this as a dict of arrays.
        self.auxValues = {}
 
        super().__init__(**kwargs)
        self.requiredAttributes.update(['badAmps', 'inputExpIdPairs', 'expIdMask', 'rawExpTimes',
                                        'rawMeans', 'rawVars', 'rowMeanVariance', 'gain',
                                        'gainErr', 'noise', 'noiseErr', 'noiseList',
                                        'ptcFitPars', 'ptcFitParsError', 'ptcFitChiSq', 'ptcTurnoff',
                                        'aMatrixNoB', 'covariances', 'covariancesModel',
                                        'covariancesSqrtWeights', 'covariancesModelNoB',
                                        'aMatrix', 'bMatrix', 'noiseMatrix', 'noiseMatrixNoB', 'finalVars',
                                        'finalModelVars', 'finalMeans', 'photoCharges', 'histVars',
                                        'histChi2Dofs', 'kspValues', 'auxValues'])
 
        self.updateMetadata(setCalibInfo=True, setCalibId=True, **kwargs)
        self._validateCovarianceMatrizSizes()
 

Member Function Documentation

_validateCovarianceMatrizSizes()

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset._validateCovarianceMatrizSizes ( self )

protected

Ensure  covMatrixSideFullCovFit <= covMatrixSide.

Definition at line 877 of file ptcDataset.py.

    def _validateCovarianceMatrizSizes(self):
        """Ensure  covMatrixSideFullCovFit <= covMatrixSide."""
        if self.covMatrixSideFullCovFit > self.covMatrixSide:
            self.log.warning("covMatrixSideFullCovFit > covMatrixSide "
                             f"({self.covMatrixSideFullCovFit} > {self.covMatrixSide})."
                             "Setting the former to the latter.")
            self.covMatrixSideFullCovFit = self.covMatrixSide

fromDetector()

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.fromDetector	(		self,
			detector )

Read metadata parameters from a detector.

Parameters
----------
detector : `lsst.afw.cameraGeom.detector`
    Input detector with parameters to use.

Returns
-------
calib : `lsst.ip.isr.PhotonTransferCurveDataset`
    The calibration constructed from the detector.

Reimplemented from lsst.ip.isr.calibType.IsrCalib.

Definition at line 769 of file ptcDataset.py.

    def fromDetector(self, detector):
        """Read metadata parameters from a detector.
 
        Parameters
        ----------
        detector : `lsst.afw.cameraGeom.detector`
            Input detector with parameters to use.
 
        Returns
        -------
        calib : `lsst.ip.isr.PhotonTransferCurveDataset`
            The calibration constructed from the detector.
        """
 
        pass
 

fromDict()

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.fromDict	(		cls,
			dictionary )

Construct a calibration from a dictionary of properties.
Must be implemented by the specific calibration subclasses.

Parameters
----------
dictionary : `dict`
    Dictionary of properties.

Returns
-------
calib : `lsst.ip.isr.PhotonTransferCurveDataset`
    Constructed calibration.

Raises
------
RuntimeError
    Raised if the supplied dictionary is for a different
    calibration.

Reimplemented from lsst.ip.isr.calibType.IsrCalib.

Definition at line 377 of file ptcDataset.py.

    def fromDict(cls, dictionary):
        """Construct a calibration from a dictionary of properties.
        Must be implemented by the specific calibration subclasses.
 
        Parameters
        ----------
        dictionary : `dict`
            Dictionary of properties.
 
        Returns
        -------
        calib : `lsst.ip.isr.PhotonTransferCurveDataset`
            Constructed calibration.
 
        Raises
        ------
        RuntimeError
            Raised if the supplied dictionary is for a different
            calibration.
        """
        calib = cls()
        if calib._OBSTYPE != dictionary['metadata']['OBSTYPE']:
            raise RuntimeError(f"Incorrect Photon Transfer Curve dataset  supplied. "
                               f"Expected {calib._OBSTYPE}, found {dictionary['metadata']['OBSTYPE']}")
        calib.setMetadata(dictionary['metadata'])
        calib.ptcFitType = dictionary['ptcFitType']
        calib.covMatrixSide = dictionary['covMatrixSide']
        calib.covMatrixSideFullCovFit = dictionary['covMatrixSideFullCovFit']
        calib.badAmps = np.array(dictionary['badAmps'], 'str').tolist()
        calib.ampNames = []
 
        # The cov matrices are square
        covMatrixSide = calib.covMatrixSide
        covMatrixSideFullCovFit = calib.covMatrixSideFullCovFit
        # Number of final signal levels
        covDimensionsProduct = len(np.array(list(dictionary['covariances'].values())[0]).ravel())
        nSignalPoints = int(covDimensionsProduct/(covMatrixSide*covMatrixSide))
 
        for ampName in dictionary['ampNames']:
            calib.ampNames.append(ampName)
            calib.inputExpIdPairs[ampName] = dictionary['inputExpIdPairs'][ampName]
            calib.expIdMask[ampName] = np.array(dictionary['expIdMask'][ampName])
            calib.rawExpTimes[ampName] = np.array(dictionary['rawExpTimes'][ampName], dtype=np.float64)
            calib.rawMeans[ampName] = np.array(dictionary['rawMeans'][ampName], dtype=np.float64)
            calib.rawVars[ampName] = np.array(dictionary['rawVars'][ampName], dtype=np.float64)
            calib.rowMeanVariance[ampName] = np.array(dictionary['rowMeanVariance'][ampName],
                                                      dtype=np.float64)
            calib.gain[ampName] = float(dictionary['gain'][ampName])
            calib.gainErr[ampName] = float(dictionary['gainErr'][ampName])
            calib.noiseList[ampName] = np.array(dictionary['noiseList'][ampName], dtype=np.float64)
            calib.noise[ampName] = float(dictionary['noise'][ampName])
            calib.noiseErr[ampName] = float(dictionary['noiseErr'][ampName])
            calib.histVars[ampName] = np.array(dictionary['histVars'][ampName], dtype=np.float64)
            calib.histChi2Dofs[ampName] = np.array(dictionary['histChi2Dofs'][ampName], dtype=np.float64)
            calib.kspValues[ampName] = np.array(dictionary['kspValues'][ampName], dtype=np.float64)
            calib.ptcFitPars[ampName] = np.array(dictionary['ptcFitPars'][ampName], dtype=np.float64)
            calib.ptcFitParsError[ampName] = np.array(dictionary['ptcFitParsError'][ampName],
                                                      dtype=np.float64)
            calib.ptcFitChiSq[ampName] = float(dictionary['ptcFitChiSq'][ampName])
            calib.ptcTurnoff[ampName] = float(dictionary['ptcTurnoff'][ampName])
            if nSignalPoints > 0:
                # Regular dataset
                calib.covariances[ampName] = np.array(dictionary['covariances'][ampName],
                                                      dtype=np.float64).reshape(
                    (nSignalPoints, covMatrixSide, covMatrixSide))
                calib.covariancesModel[ampName] = np.array(
                    dictionary['covariancesModel'][ampName],
                    dtype=np.float64).reshape(
                        (nSignalPoints, covMatrixSideFullCovFit, covMatrixSideFullCovFit))
                calib.covariancesSqrtWeights[ampName] = np.array(
                    dictionary['covariancesSqrtWeights'][ampName],
                    dtype=np.float64).reshape(
                        (nSignalPoints, covMatrixSide, covMatrixSide))
                calib.aMatrix[ampName] = np.array(dictionary['aMatrix'][ampName],
                                                  dtype=np.float64).reshape(
                    (covMatrixSideFullCovFit, covMatrixSideFullCovFit))
                calib.bMatrix[ampName] = np.array(dictionary['bMatrix'][ampName],
                                                  dtype=np.float64).reshape(
                    (covMatrixSideFullCovFit, covMatrixSideFullCovFit))
                calib.covariancesModelNoB[ampName] = np.array(
                    dictionary['covariancesModelNoB'][ampName], dtype=np.float64).reshape(
                        (nSignalPoints, covMatrixSideFullCovFit, covMatrixSideFullCovFit))
                calib.aMatrixNoB[ampName] = np.array(
                    dictionary['aMatrixNoB'][ampName],
                    dtype=np.float64).reshape((covMatrixSideFullCovFit, covMatrixSideFullCovFit))
                calib.noiseMatrix[ampName] = np.array(
                    dictionary['noiseMatrix'][ampName],
                    dtype=np.float64).reshape((covMatrixSideFullCovFit, covMatrixSideFullCovFit))
                calib.noiseMatrixNoB[ampName] = np.array(
                    dictionary['noiseMatrixNoB'][ampName],
                    dtype=np.float64).reshape((covMatrixSideFullCovFit, covMatrixSideFullCovFit))
            else:
                # Empty dataset
                calib.covariances[ampName] = np.array([], dtype=np.float64)
                calib.covariancesModel[ampName] = np.array([], dtype=np.float64)
                calib.covariancesSqrtWeights[ampName] = np.array([], dtype=np.float64)
                calib.aMatrix[ampName] = np.array([], dtype=np.float64)
                calib.bMatrix[ampName] = np.array([], dtype=np.float64)
                calib.covariancesModelNoB[ampName] = np.array([], dtype=np.float64)
                calib.aMatrixNoB[ampName] = np.array([], dtype=np.float64)
                calib.noiseMatrix[ampName] = np.array([], dtype=np.float64)
                calib.noiseMatrixNoB[ampName] = np.array([], dtype=np.float64)
 
            calib.finalVars[ampName] = np.array(dictionary['finalVars'][ampName], dtype=np.float64)
            calib.finalModelVars[ampName] = np.array(dictionary['finalModelVars'][ampName], dtype=np.float64)
            calib.finalMeans[ampName] = np.array(dictionary['finalMeans'][ampName], dtype=np.float64)
            calib.photoCharges[ampName] = np.array(dictionary['photoCharges'][ampName], dtype=np.float64)
 
        for key, value in dictionary['auxValues'].items():
            calib.auxValues[key] = np.atleast_1d(np.array(value, dtype=np.float64))
 
        calib.updateMetadata()
        return calib
 

fromTable()

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.fromTable	(		cls,
			tableList )

Construct calibration from a list of tables.
This method uses the `fromDict` method to create the
calibration, after constructing an appropriate dictionary from
the input tables.

Parameters
----------
tableList : `list` [`lsst.afw.table.Table`]
    List of tables to use to construct the datasetPtc.

Returns
-------
calib : `lsst.ip.isr.PhotonTransferCurveDataset`
    The calibration defined in the tables.

Reimplemented from lsst.ip.isr.calibType.IsrCalib.

Definition at line 555 of file ptcDataset.py.

    def fromTable(cls, tableList):
        """Construct calibration from a list of tables.
        This method uses the `fromDict` method to create the
        calibration, after constructing an appropriate dictionary from
        the input tables.
 
        Parameters
        ----------
        tableList : `list` [`lsst.afw.table.Table`]
            List of tables to use to construct the datasetPtc.
 
        Returns
        -------
        calib : `lsst.ip.isr.PhotonTransferCurveDataset`
            The calibration defined in the tables.
        """
        ptcTable = tableList[0]
 
        metadata = ptcTable.meta
        inDict = dict()
        inDict['metadata'] = metadata
        inDict['ampNames'] = []
        inDict['ptcFitType'] = []
        inDict['covMatrixSide'] = []
        inDict['covMatrixSideFullCovFit'] = []
        inDict['inputExpIdPairs'] = dict()
        inDict['expIdMask'] = dict()
        inDict['rawExpTimes'] = dict()
        inDict['rawMeans'] = dict()
        inDict['rawVars'] = dict()
        inDict['rowMeanVariance'] = dict()
        inDict['gain'] = dict()
        inDict['gainErr'] = dict()
        inDict['noiseList'] = dict()
        inDict['noise'] = dict()
        inDict['noiseErr'] = dict()
        inDict['histVars'] = dict()
        inDict['histChi2Dofs'] = dict()
        inDict['kspValues'] = dict()
        inDict['ptcFitPars'] = dict()
        inDict['ptcFitParsError'] = dict()
        inDict['ptcFitChiSq'] = dict()
        inDict['ptcTurnoff'] = dict()
        inDict['covariances'] = dict()
        inDict['covariancesModel'] = dict()
        inDict['covariancesSqrtWeights'] = dict()
        inDict['aMatrix'] = dict()
        inDict['bMatrix'] = dict()
        inDict['noiseMatrix'] = dict()
        inDict['covariancesModelNoB'] = dict()
        inDict['aMatrixNoB'] = dict()
        inDict['noiseMatrixNoB'] = dict()
        inDict['finalVars'] = dict()
        inDict['finalModelVars'] = dict()
        inDict['finalMeans'] = dict()
        inDict['badAmps'] = []
        inDict['photoCharges'] = dict()
 
        calibVersion = metadata['PTC_VERSION']
        if calibVersion == 1.0:
            cls().log.warning(f"Previous version found for PTC dataset: {calibVersion}. "
                              f"Setting 'ptcTurnoff' in all amps to last value in 'finalMeans'.")
        for record in ptcTable:
            ampName = record['AMPLIFIER_NAME']
 
            inDict['ptcFitType'] = record['PTC_FIT_TYPE']
            inDict['covMatrixSide'] = record['COV_MATRIX_SIDE']
            inDict['ampNames'].append(ampName)
            inDict['inputExpIdPairs'][ampName] = record['INPUT_EXP_ID_PAIRS'].tolist()
            inDict['expIdMask'][ampName] = record['EXP_ID_MASK']
            inDict['rawExpTimes'][ampName] = record['RAW_EXP_TIMES']
            inDict['rawMeans'][ampName] = record['RAW_MEANS']
            inDict['rawVars'][ampName] = record['RAW_VARS']
            inDict['gain'][ampName] = record['GAIN']
            inDict['gainErr'][ampName] = record['GAIN_ERR']
            inDict['noise'][ampName] = record['NOISE']
            inDict['noiseErr'][ampName] = record['NOISE_ERR']
            inDict['ptcFitPars'][ampName] = record['PTC_FIT_PARS']
            inDict['ptcFitParsError'][ampName] = record['PTC_FIT_PARS_ERROR']
            inDict['ptcFitChiSq'][ampName] = record['PTC_FIT_CHI_SQ']
            inDict['covariances'][ampName] = record['COVARIANCES']
            inDict['covariancesModel'][ampName] = record['COVARIANCES_MODEL']
            inDict['covariancesSqrtWeights'][ampName] = record['COVARIANCES_SQRT_WEIGHTS']
            inDict['aMatrix'][ampName] = record['A_MATRIX']
            inDict['bMatrix'][ampName] = record['B_MATRIX']
            inDict['covariancesModelNoB'][ampName] = record['COVARIANCES_MODEL_NO_B']
            inDict['aMatrixNoB'][ampName] = record['A_MATRIX_NO_B']
            inDict['finalVars'][ampName] = record['FINAL_VARS']
            inDict['finalModelVars'][ampName] = record['FINAL_MODEL_VARS']
            inDict['finalMeans'][ampName] = record['FINAL_MEANS']
            inDict['badAmps'] = record['BAD_AMPS'].tolist()
            inDict['photoCharges'][ampName] = record['PHOTO_CHARGE']
            if calibVersion == 1.0:
                mask = record['FINAL_MEANS'].mask
                array = record['FINAL_MEANS'][~mask]
                if len(array) > 0:
                    inDict['ptcTurnoff'][ampName] = record['FINAL_MEANS'][~mask][-1]
                else:
                    inDict['ptcTurnoff'][ampName] = np.nan
            else:
                inDict['ptcTurnoff'][ampName] = record['PTC_TURNOFF']
            if calibVersion < 1.2:
                inDict['histVars'][ampName] = np.array([np.nan])
                inDict['histChi2Dofs'][ampName] = np.array([np.nan])
                inDict['kspValues'][ampName] = np.array([0.0])
            else:
                inDict['histVars'][ampName] = record['HIST_VARS']
                inDict['histChi2Dofs'][ampName] = record['HIST_CHI2_DOFS']
                inDict['kspValues'][ampName] = record['KS_PVALUES']
            if calibVersion < 1.3:
                nanMatrix = np.full_like(inDict['aMatrix'][ampName], np.nan)
                inDict['noiseMatrix'][ampName] = nanMatrix
                inDict['noiseMatrixNoB'][ampName] = nanMatrix
            else:
                inDict['noiseMatrix'][ampName] = record['NOISE_MATRIX']
                inDict['noiseMatrixNoB'][ampName] = record['NOISE_MATRIX_NO_B']
            if calibVersion < 1.5:
                # Matched to `COV_MATRIX_SIDE`. Same for all amps.
                inDict['covMatrixSideFullCovFit'] = inDict['covMatrixSide']
            else:
                inDict['covMatrixSideFullCovFit'] = record['COV_MATRIX_SIDE_FULL_COV_FIT']
            if calibVersion < 1.6:
                inDict['rowMeanVariance'][ampName] = np.full((len(inDict['expIdMask'][ampName]),), np.nan)
            else:
                inDict['rowMeanVariance'][ampName] = record['ROW_MEAN_VARIANCE']
            if calibVersion < 1.7:
                inDict['noiseList'][ampName] = np.full_like(inDict['rawMeans'][ampName], np.nan)
            else:
                inDict['noiseList'][ampName] = record['NOISE_LIST']
 
        inDict['auxValues'] = {}
        record = ptcTable[0]
        for col in record.columns.keys():
            if col.startswith('PTCAUX_'):
                parts = col.split('PTCAUX_')
                inDict['auxValues'][parts[1]] = record[col]
 
        return cls().fromDict(inDict)
 

getExpIdsUsed()

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.getExpIdsUsed	(		self,
			ampName )

Get the exposures used, i.e. not discarded, for a given amp.
If no mask has been created yet, all exposures are returned.

Parameters
----------
ampName : `str`

Returns
-------
expIdsUsed : `list` [`tuple`]
    List of pairs of exposure ids used in PTC.

Definition at line 785 of file ptcDataset.py.

    def getExpIdsUsed(self, ampName):
        """Get the exposures used, i.e. not discarded, for a given amp.
        If no mask has been created yet, all exposures are returned.
 
        Parameters
        ----------
        ampName : `str`
 
        Returns
        -------
        expIdsUsed : `list` [`tuple`]
            List of pairs of exposure ids used in PTC.
        """
        if len(self.expIdMask[ampName]) == 0:
            return self.inputExpIdPairs[ampName]
 
        # if the mask exists it had better be the same length as the expIdPairs
        assert len(self.expIdMask[ampName]) == len(self.inputExpIdPairs[ampName])
 
        pairs = self.inputExpIdPairs[ampName]
        mask = self.expIdMask[ampName]
        # cast to bool required because numpy
        try:
            expIdsUsed = [(exp1, exp2) for ((exp1, exp2), m) in zip(pairs, mask) if m]
        except ValueError:
            self.log.warning("The PTC file was written incorrectly; you should rerun the "
                             "PTC solve task if possible.")
            expIdsUsed = []
            for pairList, m in zip(pairs, mask):
                if m:
                    expIdsUsed.append(pairList[0])
 
        return expIdsUsed
 

getGoodAmps()

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.getGoodAmps

(

self

)

Get the good amps from this PTC.

Definition at line 819 of file ptcDataset.py.

    def getGoodAmps(self):
        """Get the good amps from this PTC."""
        return [amp for amp in self.ampNames if amp not in self.badAmps]
 

getGoodPoints()

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.getGoodPoints	(		self,
			ampName )

Get the good points used for a given amp in the PTC.

Parameters
----------
ampName : `str`
    Amplifier's name.

Returns
-------
goodPoints : `np.ndarray`
    Boolean array of good points used in PTC.

Definition at line 823 of file ptcDataset.py.

    def getGoodPoints(self, ampName):
        """Get the good points used for a given amp in the PTC.
 
        Parameters
        ----------
        ampName : `str`
            Amplifier's name.
 
        Returns
        -------
        goodPoints : `np.ndarray`
            Boolean array of good points used in PTC.
        """
        return self.expIdMask[ampName]
 

setAmpValuesPartialDataset()

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.setAmpValuesPartialDataset	(		self,
			ampName,
			inputExpIdPair = (-1, -1),
			rawExpTime = np.nan,
			rawMean = np.nan,
			rawVar = np.nan,
			rowMeanVariance = np.nan,
			photoCharge = np.nan,
			expIdMask = False,
			covariance = None,
			covSqrtWeights = None,
			gain = np.nan,
			noise = np.nan,
			histVar = np.nan,
			histChi2Dof = np.nan,
			kspValue = 0.0,
			auxValues = None )

Set the amp values for a partial PTC Dataset (from cpExtractPtcTask).

Parameters
----------
ampName : `str`
    Name of the amp to set the values.
inputExpIdPair : `tuple` [`int`]
    Exposure IDs of input pair.
rawExpTime : `float`, optional
    Exposure time for this exposure pair.
rawMean : `float`, optional
    Average of the means of the exposures in this pair.
rawVar : `float`, optional
    Variance of the difference of the exposures in this pair.
rowMeanVariance : `float`, optional
    Variance of the means of the rows in the difference image
    of the exposures in this pair.
photoCharge : `float`, optional
    Integrated photocharge for flat pair for linearity calibration.
expIdMask : `bool`, optional
    Flag setting if this exposure pair should be used (True)
    or not used (False).
covariance : `np.ndarray` or None, optional
    Measured covariance for this exposure pair.
covSqrtWeights : `np.ndarray` or None, optional
    Measured sqrt of covariance weights in this exposure pair.
gain : `float`, optional
    Estimated gain for this exposure pair.
noise : `float`, optional
    Estimated read noise for this exposure pair.
histVar : `float`, optional
    Variance estimated from fitting a histogram with a Gaussian model.
histChi2Dof : `float`, optional
    Chi-squared per degree of freedom from Gaussian histogram fit.
kspValue : `float`, optional
    KS test p-value from the Gaussian histogram fit.

Definition at line 260 of file ptcDataset.py.

    ):
        """
        Set the amp values for a partial PTC Dataset (from cpExtractPtcTask).
 
        Parameters
        ----------
        ampName : `str`
            Name of the amp to set the values.
        inputExpIdPair : `tuple` [`int`]
            Exposure IDs of input pair.
        rawExpTime : `float`, optional
            Exposure time for this exposure pair.
        rawMean : `float`, optional
            Average of the means of the exposures in this pair.
        rawVar : `float`, optional
            Variance of the difference of the exposures in this pair.
        rowMeanVariance : `float`, optional
            Variance of the means of the rows in the difference image
            of the exposures in this pair.
        photoCharge : `float`, optional
            Integrated photocharge for flat pair for linearity calibration.
        expIdMask : `bool`, optional
            Flag setting if this exposure pair should be used (True)
            or not used (False).
        covariance : `np.ndarray` or None, optional
            Measured covariance for this exposure pair.
        covSqrtWeights : `np.ndarray` or None, optional
            Measured sqrt of covariance weights in this exposure pair.
        gain : `float`, optional
            Estimated gain for this exposure pair.
        noise : `float`, optional
            Estimated read noise for this exposure pair.
        histVar : `float`, optional
            Variance estimated from fitting a histogram with a Gaussian model.
        histChi2Dof : `float`, optional
            Chi-squared per degree of freedom from Gaussian histogram fit.
        kspValue : `float`, optional
            KS test p-value from the Gaussian histogram fit.
        """
        nanMatrix = np.full((self.covMatrixSide, self.covMatrixSide), np.nan)
        nanMatrixFit = np.full((self.covMatrixSideFullCovFit,
                               self.covMatrixSideFullCovFit), np.nan)
        if covariance is None:
            covariance = nanMatrix
        if covSqrtWeights is None:
            covSqrtWeights = nanMatrix
 
        self.inputExpIdPairs[ampName] = [inputExpIdPair]
        self.rawExpTimes[ampName] = np.array([rawExpTime])
        self.rawMeans[ampName] = np.array([rawMean])
        self.rawVars[ampName] = np.array([rawVar])
        self.rowMeanVariance[ampName] = np.array([rowMeanVariance])
        self.photoCharges[ampName] = np.array([photoCharge])
        self.expIdMask[ampName] = np.array([expIdMask])
        self.covariances[ampName] = np.array([covariance])
        self.covariancesSqrtWeights[ampName] = np.array([covSqrtWeights])
        self.gain[ampName] = gain
        self.noise[ampName] = noise
        self.histVars[ampName] = np.array([histVar])
        self.histChi2Dofs[ampName] = np.array([histChi2Dof])
        self.kspValues[ampName] = np.array([kspValue])
 
        # From FULLCOVARIANCE model
        self.covariancesModel[ampName] = np.array([nanMatrixFit])
        self.covariancesModelNoB[ampName] = np.array([nanMatrixFit])
        self.aMatrix[ampName] = nanMatrixFit
        self.bMatrix[ampName] = nanMatrixFit
        self.aMatrixNoB[ampName] = nanMatrixFit
        self.noiseMatrix[ampName] = nanMatrixFit
        self.noiseMatrixNoB[ampName] = nanMatrixFit
 

setAuxValuesPartialDataset()

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.setAuxValuesPartialDataset	(		self,
			auxDict )

Set a dictionary of auxiliary values for a partial dataset.

Parameters
----------
auxDict : `dict` [`str`, `float`]
    Dictionary of float values.

Definition at line 349 of file ptcDataset.py.

    def setAuxValuesPartialDataset(self, auxDict):
        """
        Set a dictionary of auxiliary values for a partial dataset.
 
        Parameters
        ----------
        auxDict : `dict` [`str`, `float`]
            Dictionary of float values.
        """
        for key, value in auxDict.items():
            self.auxValues[key] = np.atleast_1d(np.array(value, dtype=np.float64))
 

toDict()

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.toDict

(

self

)

Return a dictionary containing the calibration properties.
The dictionary should be able to be round-tripped through
`fromDict`.

Returns
-------
dictionary : `dict`
    Dictionary of properties.

Reimplemented from lsst.ip.isr.calibType.IsrCalib.

Definition at line 491 of file ptcDataset.py.

    def toDict(self):
        """Return a dictionary containing the calibration properties.
        The dictionary should be able to be round-tripped through
        `fromDict`.
 
        Returns
        -------
        dictionary : `dict`
            Dictionary of properties.
        """
        self.updateMetadata()
 
        outDict = dict()
        metadata = self.getMetadata()
        outDict['metadata'] = metadata
 
        def _dictOfArraysToDictOfLists(dictOfArrays):
            dictOfLists = {}
            for key, value in dictOfArrays.items():
                dictOfLists[key] = value.ravel().tolist()
 
            return dictOfLists
 
        outDict['ptcFitType'] = self.ptcFitType
        outDict['covMatrixSide'] = self.covMatrixSide
        outDict['covMatrixSideFullCovFit'] = self.covMatrixSideFullCovFit
        outDict['ampNames'] = self.ampNames
        outDict['badAmps'] = self.badAmps
        outDict['inputExpIdPairs'] = self.inputExpIdPairs
        outDict['expIdMask'] = _dictOfArraysToDictOfLists(self.expIdMask)
        outDict['rawExpTimes'] = _dictOfArraysToDictOfLists(self.rawExpTimes)
        outDict['rawMeans'] = _dictOfArraysToDictOfLists(self.rawMeans)
        outDict['rawVars'] = _dictOfArraysToDictOfLists(self.rawVars)
        outDict['rowMeanVariance'] = _dictOfArraysToDictOfLists(self.rowMeanVariance)
        outDict['gain'] = self.gain
        outDict['gainErr'] = self.gainErr
        outDict['noiseList'] = _dictOfArraysToDictOfLists(self.noiseList)
        outDict['noise'] = self.noise
        outDict['noiseErr'] = self.noiseErr
        outDict['histVars'] = self.histVars
        outDict['histChi2Dofs'] = self.histChi2Dofs
        outDict['kspValues'] = self.kspValues
        outDict['ptcFitPars'] = _dictOfArraysToDictOfLists(self.ptcFitPars)
        outDict['ptcFitParsError'] = _dictOfArraysToDictOfLists(self.ptcFitParsError)
        outDict['ptcFitChiSq'] = self.ptcFitChiSq
        outDict['ptcTurnoff'] = self.ptcTurnoff
        outDict['covariances'] = _dictOfArraysToDictOfLists(self.covariances)
        outDict['covariancesModel'] = _dictOfArraysToDictOfLists(self.covariancesModel)
        outDict['covariancesSqrtWeights'] = _dictOfArraysToDictOfLists(self.covariancesSqrtWeights)
        outDict['aMatrix'] = _dictOfArraysToDictOfLists(self.aMatrix)
        outDict['bMatrix'] = _dictOfArraysToDictOfLists(self.bMatrix)
        outDict['noiseMatrix'] = _dictOfArraysToDictOfLists(self.noiseMatrix)
        outDict['covariancesModelNoB'] = _dictOfArraysToDictOfLists(self.covariancesModelNoB)
        outDict['aMatrixNoB'] = _dictOfArraysToDictOfLists(self.aMatrixNoB)
        outDict['noiseMatrixNoB'] = _dictOfArraysToDictOfLists(self.noiseMatrixNoB)
        outDict['finalVars'] = _dictOfArraysToDictOfLists(self.finalVars)
        outDict['finalModelVars'] = _dictOfArraysToDictOfLists(self.finalModelVars)
        outDict['finalMeans'] = _dictOfArraysToDictOfLists(self.finalMeans)
        outDict['photoCharges'] = _dictOfArraysToDictOfLists(self.photoCharges)
        outDict['auxValues'] = _dictOfArraysToDictOfLists(self.auxValues)
 
        return outDict
 

toTable()

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.toTable

(

self

)

Construct a list of tables containing the information in this
calibration.

The list of tables should create an identical calibration
after being passed to this class's fromTable method.

Returns
-------
tableList : `list` [`astropy.table.Table`]
    List of tables containing the linearity calibration
    information.

Reimplemented from lsst.ip.isr.calibType.IsrCalib.

Definition at line 694 of file ptcDataset.py.

    def toTable(self):
        """Construct a list of tables containing the information in this
        calibration.
 
        The list of tables should create an identical calibration
        after being passed to this class's fromTable method.
 
        Returns
        -------
        tableList : `list` [`astropy.table.Table`]
            List of tables containing the linearity calibration
            information.
        """
        tableList = []
        self.updateMetadata()
 
        badAmps = np.array(self.badAmps) if len(self.badAmps) else np.array([], dtype="U3")
 
        catalogList = []
        for ampName in self.ampNames:
            ampDict = {
                'AMPLIFIER_NAME': ampName,
                'PTC_FIT_TYPE': self.ptcFitType,
                'COV_MATRIX_SIDE': self.covMatrixSide,
                'COV_MATRIX_SIDE_FULL_COV_FIT': self.covMatrixSideFullCovFit,
                'INPUT_EXP_ID_PAIRS': self.inputExpIdPairs[ampName],
                'EXP_ID_MASK': self.expIdMask[ampName],
                'RAW_EXP_TIMES': self.rawExpTimes[ampName],
                'RAW_MEANS': self.rawMeans[ampName],
                'RAW_VARS': self.rawVars[ampName],
                'ROW_MEAN_VARIANCE': self.rowMeanVariance[ampName],
                'GAIN': self.gain[ampName],
                'GAIN_ERR': self.gainErr[ampName],
                'NOISE_LIST': self.noiseList[ampName],
                'NOISE': self.noise[ampName],
                'NOISE_ERR': self.noiseErr[ampName],
                'HIST_VARS': self.histVars[ampName],
                'HIST_CHI2_DOFS': self.histChi2Dofs[ampName],
                'KS_PVALUES': self.kspValues[ampName],
                'PTC_FIT_PARS': np.array(self.ptcFitPars[ampName]),
                'PTC_FIT_PARS_ERROR': np.array(self.ptcFitParsError[ampName]),
                'PTC_FIT_CHI_SQ': self.ptcFitChiSq[ampName],
                'PTC_TURNOFF': self.ptcTurnoff[ampName],
                'A_MATRIX': self.aMatrix[ampName].ravel(),
                'B_MATRIX': self.bMatrix[ampName].ravel(),
                'A_MATRIX_NO_B': self.aMatrixNoB[ampName].ravel(),
                'NOISE_MATRIX': self.noiseMatrix[ampName].ravel(),
                'NOISE_MATRIX_NO_B': self.noiseMatrixNoB[ampName].ravel(),
                'BAD_AMPS': badAmps,
                'PHOTO_CHARGE': self.photoCharges[ampName],
                'COVARIANCES': self.covariances[ampName].ravel(),
                'COVARIANCES_MODEL': self.covariancesModel[ampName].ravel(),
                'COVARIANCES_SQRT_WEIGHTS': self.covariancesSqrtWeights[ampName].ravel(),
                'COVARIANCES_MODEL_NO_B': self.covariancesModelNoB[ampName].ravel(),
                'FINAL_VARS': self.finalVars[ampName],
                'FINAL_MODEL_VARS': self.finalModelVars[ampName],
                'FINAL_MEANS': self.finalMeans[ampName],
            }
 
            if self.auxValues:
                for key, value in self.auxValues.items():
                    ampDict[f"PTCAUX_{key}"] = value
 
            catalogList.append(ampDict)
 
        catalog = Table(catalogList)
 
        inMeta = self.getMetadata().toDict()
        outMeta = {k: v for k, v in inMeta.items() if v is not None}
        outMeta.update({k: "" for k, v in inMeta.items() if v is None})
        catalog.meta = outMeta
        tableList.append(catalog)
 
        return tableList
 

updateMetadata()

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.updateMetadata	(		self,
		**	kwargs )

Update calibration metadata.
This calls the base class's method after ensuring the required
calibration keywords will be saved.

Parameters
----------
setDate : `bool`, optional
    Update the CALIBDATE fields in the metadata to the current
    time. Defaults to False.
kwargs :
    Other keyword parameters to set in the metadata.

Reimplemented from lsst.ip.isr.calibType.IsrCalib.

Definition at line 361 of file ptcDataset.py.

    def updateMetadata(self, **kwargs):
        """Update calibration metadata.
        This calls the base class's method after ensuring the required
        calibration keywords will be saved.
 
        Parameters
        ----------
        setDate : `bool`, optional
            Update the CALIBDATE fields in the metadata to the current
            time. Defaults to False.
        kwargs :
            Other keyword parameters to set in the metadata.
        """
        super().updateMetadata(PTC_FIT_TYPE=self.ptcFitType, **kwargs)
 

validateGainNoiseTurnoffValues()

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.validateGainNoiseTurnoffValues	(		self,
			ampName,
			doWarn = False )

Ensure the gain, read noise, and PTC turnoff have
sensible values.

Parameters
----------
ampName : `str`
    Amplifier's name.

Definition at line 838 of file ptcDataset.py.

    def validateGainNoiseTurnoffValues(self, ampName, doWarn=False):
        """Ensure the gain, read noise, and PTC turnoff have
        sensible values.
 
        Parameters
        ----------
        ampName : `str`
            Amplifier's name.
        """
 
        gain = self.gain[ampName]
        noise = self.noise[ampName]
        ptcTurnoff = self.ptcTurnoff[ampName]
 
        # Check if gain is not positive or is np.nan
        if not (isinstance(gain, (int, float)) and gain > 0) or math.isnan(gain):
            if doWarn:
                self.log.warning(f"Invalid gain value {gain}"
                                 " Setting to default: Gain=1")
            gain = 1
 
        # Check if noise is not positive or is np.nan
        if not (isinstance(noise, (int, float)) and noise > 0) or math.isnan(noise):
            if doWarn:
                self.log.warning(f"Invalid noise value: {noise}"
                                 " Setting to default: Noise=1")
            noise = 1
 
        # Check if ptcTurnoff is not positive or is np.nan
        if not (isinstance(ptcTurnoff, (int, float)) and ptcTurnoff > 0) or math.isnan(ptcTurnoff):
            if doWarn:
                self.log.warning(f"Invalid PTC turnoff value: {ptcTurnoff}"
                                 " Setting to default: PTC Turnoff=2e19")
            ptcTurnoff = 2e19
 
        self.gain[ampName] = gain
        self.noise[ampName] = noise
        self.ptcTurnoff[ampName] = ptcTurnoff
 

Member Data Documentation

_OBSTYPE

str lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset._OBSTYPE = 'PTC'

staticprotected

Definition at line 190 of file ptcDataset.py.

_SCHEMA

str lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset._SCHEMA = 'Gen3 Photon Transfer Curve'

staticprotected

Definition at line 191 of file ptcDataset.py.

_VERSION

float lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset._VERSION = 1.7

staticprotected

Definition at line 192 of file ptcDataset.py.

aMatrix

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.aMatrix

Definition at line 232 of file ptcDataset.py.

aMatrixNoB

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.aMatrixNoB

Definition at line 236 of file ptcDataset.py.

ampNames

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.ampNames

Definition at line 197 of file ptcDataset.py.

auxValues

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.auxValues

Definition at line 244 of file ptcDataset.py.

badAmps

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.badAmps

Definition at line 204 of file ptcDataset.py.

bMatrix

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.bMatrix

Definition at line 233 of file ptcDataset.py.

covariances

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.covariances

Definition at line 229 of file ptcDataset.py.

covariancesModel

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.covariancesModel

Definition at line 230 of file ptcDataset.py.

covariancesModelNoB

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.covariancesModelNoB

Definition at line 235 of file ptcDataset.py.

covariancesSqrtWeights

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.covariancesSqrtWeights

Definition at line 231 of file ptcDataset.py.

covMatrixSide

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.covMatrixSide

Definition at line 198 of file ptcDataset.py.

covMatrixSideFullCovFit

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.covMatrixSideFullCovFit

Definition at line 200 of file ptcDataset.py.

expIdMask

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.expIdMask

Definition at line 207 of file ptcDataset.py.

finalMeans

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.finalMeans

Definition at line 241 of file ptcDataset.py.

finalModelVars

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.finalModelVars

Definition at line 240 of file ptcDataset.py.

finalVars

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.finalVars

Definition at line 239 of file ptcDataset.py.

gain

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.gain

Definition at line 214 of file ptcDataset.py.

gainErr

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.gainErr

Definition at line 215 of file ptcDataset.py.

histChi2Dofs

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.histChi2Dofs

Definition at line 221 of file ptcDataset.py.

histVars

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.histVars

Definition at line 220 of file ptcDataset.py.

inputExpIdPairs

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.inputExpIdPairs

Definition at line 206 of file ptcDataset.py.

kspValues

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.kspValues

Definition at line 222 of file ptcDataset.py.

noise

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.noise

Definition at line 217 of file ptcDataset.py.

noiseErr

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.noiseErr

Definition at line 218 of file ptcDataset.py.

noiseList

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.noiseList

Definition at line 216 of file ptcDataset.py.

noiseMatrix

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.noiseMatrix

Definition at line 234 of file ptcDataset.py.

noiseMatrixNoB

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.noiseMatrixNoB

Definition at line 237 of file ptcDataset.py.

photoCharges

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.photoCharges

Definition at line 212 of file ptcDataset.py.

ptcFitChiSq

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.ptcFitChiSq

Definition at line 226 of file ptcDataset.py.

ptcFitPars

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.ptcFitPars

Definition at line 224 of file ptcDataset.py.

ptcFitParsError

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.ptcFitParsError

Definition at line 225 of file ptcDataset.py.

ptcFitType

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.ptcFitType

Definition at line 196 of file ptcDataset.py.

ptcTurnoff

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.ptcTurnoff

Definition at line 227 of file ptcDataset.py.

rawExpTimes

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.rawExpTimes

Definition at line 208 of file ptcDataset.py.

rawMeans

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.rawMeans

Definition at line 209 of file ptcDataset.py.

rawVars

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.rawVars

Definition at line 210 of file ptcDataset.py.

rowMeanVariance

lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset.rowMeanVariance

Definition at line 211 of file ptcDataset.py.

---

The documentation for this class was generated from the following file:

• /j/snowflake/release/lsstsw/stack/lsst-scipipe-8.0.0/Linux64/ip_isr/g02d81e74bb+fa3a7a026e/python/lsst/ip/isr/ptcDataset.py