lsst.cp.pipe.ptc.PhotonTransferCurveDataset Class Reference

Public Member Functions
def	__init__ (self, ampNames, ptcFitType)
def	__setattr__ (self, attribute, value)
def	getExpIdsUsed (self, ampName)
def	getGoodAmps (self)

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`
    Type of model fitted to the PTC: "POLYNOMIAL", "EXPAPPROXIMATION", or "FULLCOVARIANCE".

Returns
-------
`lsst.cp.pipe.ptc.PhotonTransferCurveDataset`
    Output dataset from MeasurePhotonTransferCurveTask.

Definition at line 180 of file ptc.py.

Constructor & Destructor Documentation

__init__()

def lsst.cp.pipe.ptc.PhotonTransferCurveDataset.__init__	(		self,
			ampNames,
			ptcFitType
	)

Definition at line 214 of file ptc.py.

    def __init__(self, ampNames, ptcFitType):
        # add items to __dict__ directly because __setattr__ is overridden
 
        # instance variables
        self.__dict__["ptcFitType"] = ptcFitType
        self.__dict__["ampNames"] = ampNames
        self.__dict__["badAmps"] = []
 
        # raw data variables
        # expIdMask is the mask produced after outlier rejection. The mask produced by "FULLCOVARIANCE"
        # may differ from the one produced in the other two PTC fit types.
        self.__dict__["inputExpIdPairs"] = {ampName: [] for ampName in ampNames}
        self.__dict__["expIdMask"] = {ampName: [] for ampName in ampNames}
        self.__dict__["rawExpTimes"] = {ampName: [] for ampName in ampNames}
        self.__dict__["rawMeans"] = {ampName: [] for ampName in ampNames}
        self.__dict__["rawVars"] = {ampName: [] for ampName in ampNames}
        self.__dict__["photoCharge"] = {ampName: [] for ampName in ampNames}
 
        # Gain and noise
        self.__dict__["gain"] = {ampName: -1. for ampName in ampNames}
        self.__dict__["gainErr"] = {ampName: -1. for ampName in ampNames}
        self.__dict__["noise"] = {ampName: -1. for ampName in ampNames}
        self.__dict__["noiseErr"] = {ampName: -1. for ampName in ampNames}
 
        # if ptcFitTye in ["POLYNOMIAL", "EXPAPPROXIMATION"]
        # fit information
        self.__dict__["ptcFitPars"] = {ampName: [] for ampName in ampNames}
        self.__dict__["ptcFitParsError"] = {ampName: [] for ampName in ampNames}
        self.__dict__["ptcFitReducedChiSquared"] = {ampName: [] for ampName in ampNames}
 
        # if ptcFitTye in ["FULLCOVARIANCE"]
        # "covariancesTuple" is a numpy recarray with entries of the form
        # ['mu', 'i', 'j', 'var', 'cov', 'npix', 'ext', 'expTime', 'ampName']
        # "covariancesFits" has CovFit objects that fit the measured covariances to Eq. 20 of Astier+19.
        # In "covariancesFitsWithNoB", "b"=0 in the model described by Eq. 20 of Astier+19.
        self.__dict__["covariancesTuple"] = {ampName: [] for ampName in ampNames}
        self.__dict__["covariancesFitsWithNoB"] = {ampName: [] for ampName in ampNames}
        self.__dict__["covariancesFits"] = {ampName: [] for ampName in ampNames}
        self.__dict__["aMatrix"] = {ampName: [] for ampName in ampNames}
        self.__dict__["bMatrix"] = {ampName: [] for ampName in ampNames}
 
        # "final" means that the "raw" vectors above had "expIdMask" applied.
        self.__dict__["finalVars"] = {ampName: [] for ampName in ampNames}
        self.__dict__["finalModelVars"] = {ampName: [] for ampName in ampNames}
        self.__dict__["finalMeans"] = {ampName: [] for ampName in ampNames}
 

Member Function Documentation

__setattr__()

def lsst.cp.pipe.ptc.PhotonTransferCurveDataset.__setattr__	(		self,
			attribute,
			value
	)

Protect class attributes

Definition at line 260 of file ptc.py.

    def __setattr__(self, attribute, value):
        """Protect class attributes"""
        if attribute not in self.__dict__:
            raise AttributeError(f"{attribute} is not already a member of PhotonTransferCurveDataset, which"
                                 " does not support setting of new attributes.")
        else:
            self.__dict__[attribute] = value
 

getExpIdsUsed()

def lsst.cp.pipe.ptc.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.

Definition at line 268 of file ptc.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.
        """
        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
        return [(exp1, exp2) for ((exp1, exp2), m) in zip(pairs, mask) if bool(m) is True]
 

getGoodAmps()

def lsst.cp.pipe.ptc.PhotonTransferCurveDataset.getGoodAmps

(

self

)

Definition at line 284 of file ptc.py.

    def getGoodAmps(self):
        return [amp for amp in self.ampNames if amp not in self.badAmps]
 
 

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

• /j/snowflake/release/lsstsw/stack/cb4e2dc/Linux64/cp_pipe/20.0.0-17-ga9337b4+cbc55e0393/python/lsst/cp/pipe/ptc.py