lsst.cp.pipe.ptc.astierCovPtcUtils.CovFastFourierTransform Class Reference

Public Member Functions
def	__init__ (self, diff, w, fftShape, maxRangeCov)
def	cov (self, dx, dy)
def	reportCovFastFourierTransform (self, maxRange)

Public Attributes
	pCov
	pMean
	pCount

Detailed Description

A class to compute (via FFT) the nearby pixels correlation function.

Implements appendix of Astier+19.

Parameters
----------
diff: `numpy.array`
    Image where to calculate the covariances (e.g., the difference image of two flats).

w: `numpy.array`
    Weight image (mask): it should consist of 1's (good pixel) and 0's (bad pixels).

fftShape: `tuple`
    2d-tuple with the shape of the FFT

maxRangeCov: `int`
    Maximum range for the covariances.

Definition at line 28 of file astierCovPtcUtils.py.

Constructor & Destructor Documentation

__init__()

def lsst.cp.pipe.ptc.astierCovPtcUtils.CovFastFourierTransform.__init__	(		self,
			diff,
			w,
			fftShape,
			maxRangeCov
	)

Definition at line 48 of file astierCovPtcUtils.py.

    def __init__(self, diff, w, fftShape, maxRangeCov):
        # check that the zero padding implied by "fft_shape"
        # is large enough for the required correlation range
        assert(fftShape[0] > diff.shape[0]+maxRangeCov+1)
        assert(fftShape[1] > diff.shape[1]+maxRangeCov+1)
        # for some reason related to numpy.fft.rfftn,
        # the second dimension should be even, so
        if fftShape[1]%2 == 1:
            fftShape = (fftShape[0], fftShape[1]+1)
        tIm = np.fft.rfft2(diff*w, fftShape)
        tMask = np.fft.rfft2(w, fftShape)
        # sum of  "squares"
        self.pCov = np.fft.irfft2(tIm*tIm.conjugate())
        # sum of values
        self.pMean = np.fft.irfft2(tIm*tMask.conjugate())
        # number of w!=0 pixels.
        self.pCount = np.fft.irfft2(tMask*tMask.conjugate())
 

Member Function Documentation

cov()

def lsst.cp.pipe.ptc.astierCovPtcUtils.CovFastFourierTransform.cov	(		self,
			dx,
			dy
	)

Covariance for dx,dy averaged with dx,-dy if both non zero.

Implements appendix of Astier+19.

Parameters
----------
dx: `int`
   Lag in x

dy: `int
   Lag in y

Returns
-------
0.5*(cov1+cov2): `float`
    Covariance at (dx, dy) lag

npix1+npix2: `int`
    Number of pixels used in covariance calculation.

Definition at line 66 of file astierCovPtcUtils.py.

    def cov(self, dx, dy):
        """Covariance for dx,dy averaged with dx,-dy if both non zero.
 
        Implements appendix of Astier+19.
 
        Parameters
        ----------
        dx: `int`
           Lag in x
 
        dy: `int
           Lag in y
 
        Returns
        -------
        0.5*(cov1+cov2): `float`
            Covariance at (dx, dy) lag
 
        npix1+npix2: `int`
            Number of pixels used in covariance calculation.
        """
        # compensate rounding errors
        nPix1 = int(round(self.pCount[dy, dx]))
        cov1 = self.pCov[dy, dx]/nPix1-self.pMean[dy, dx]*self.pMean[-dy, -dx]/(nPix1*nPix1)
        if (dx == 0 or dy == 0):
            return cov1, nPix1
        nPix2 = int(round(self.pCount[-dy, dx]))
        cov2 = self.pCov[-dy, dx]/nPix2-self.pMean[-dy, dx]*self.pMean[dy, -dx]/(nPix2*nPix2)
        return 0.5*(cov1+cov2), nPix1+nPix2
 

reportCovFastFourierTransform()

def lsst.cp.pipe.ptc.astierCovPtcUtils.CovFastFourierTransform.reportCovFastFourierTransform	(		self,
			maxRange
	)

Produce a list of tuples with covariances.

Implements appendix of Astier+19.

Parameters
----------
maxRange: `int`
    Maximum range of covariances.

Returns
-------
tupleVec: `list`
    List with covariance tuples.

Definition at line 96 of file astierCovPtcUtils.py.

    def reportCovFastFourierTransform(self, maxRange):
        """Produce a list of tuples with covariances.
 
        Implements appendix of Astier+19.
 
        Parameters
        ----------
        maxRange: `int`
            Maximum range of covariances.
 
        Returns
        -------
        tupleVec: `list`
            List with covariance tuples.
        """
        tupleVec = []
        # (dy,dx) = (0,0) has to be first
        for dy in range(maxRange+1):
            for dx in range(maxRange+1):
                cov, npix = self.cov(dx, dy)
                if (dx == 0 and dy == 0):
                    var = cov
                tupleVec.append((dx, dy, var, cov, npix))
        return tupleVec
 
 

Member Data Documentation

pCount

lsst.cp.pipe.ptc.astierCovPtcUtils.CovFastFourierTransform.pCount

Definition at line 64 of file astierCovPtcUtils.py.

pCov

lsst.cp.pipe.ptc.astierCovPtcUtils.CovFastFourierTransform.pCov

Definition at line 60 of file astierCovPtcUtils.py.

pMean

lsst.cp.pipe.ptc.astierCovPtcUtils.CovFastFourierTransform.pMean

Definition at line 62 of file astierCovPtcUtils.py.

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

• /j/snowflake/release/lsstsw/stack/lsst-scipipe-0.6.0/Linux64/cp_pipe/22.0.1-18-gb17765a+2264247a6b/python/lsst/cp/pipe/ptc/astierCovPtcUtils.py