lsst.cp.pipe.astierCovPtcUtils.CovFft Class Reference

Public Member Functions
def	__init__ (self, diff, w, fftShape, maxRangeCov)
def	cov (self, dx, dy)
def	reportCovFft (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.astierCovPtcUtils.CovFft.__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.astierCovPtcUtils.CovFft.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
 

reportCovFft()

def lsst.cp.pipe.astierCovPtcUtils.CovFft.reportCovFft	(		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 reportCovFft(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.astierCovPtcUtils.CovFft.pCount

Definition at line 64 of file astierCovPtcUtils.py.

pCov

lsst.cp.pipe.astierCovPtcUtils.CovFft.pCov

Definition at line 60 of file astierCovPtcUtils.py.

pMean

lsst.cp.pipe.astierCovPtcUtils.CovFft.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/0.1.5/Linux64/cp_pipe/21.0.0-5-gb7080ec+8658c79ec4/python/lsst/cp/pipe/astierCovPtcUtils.py