LSST Applications
21.0.0-131-g8cabc107+528f53ee53,22.0.0+00495a2688,22.0.0+0ef2527977,22.0.0+11a2aa21cd,22.0.0+269b7e55e3,22.0.0+2c6b6677a3,22.0.0+64c1bc5aa5,22.0.0+7b3a3f865e,22.0.0+e1b6d2281c,22.0.0+ff3c34362c,22.0.1-1-g1b65d06+c95cbdf3df,22.0.1-1-g7058be7+1cf78af69b,22.0.1-1-g7dab645+2a65e40b06,22.0.1-1-g8760c09+64c1bc5aa5,22.0.1-1-g949febb+64c1bc5aa5,22.0.1-1-ga324b9c+269b7e55e3,22.0.1-1-gf9d8b05+ff3c34362c,22.0.1-10-g781e53d+9b51d1cd24,22.0.1-10-gba590ab+b9624b875d,22.0.1-13-g76f9b8d+2c6b6677a3,22.0.1-14-g22236948+57af756299,22.0.1-18-g3db9cf4b+9b7092c56c,22.0.1-18-gb17765a+2264247a6b,22.0.1-2-g8ef0a89+2c6b6677a3,22.0.1-2-gcb770ba+c99495d3c6,22.0.1-24-g2e899d296+4206820b0d,22.0.1-3-g7aa11f2+2c6b6677a3,22.0.1-3-g8c1d971+f253ffa91f,22.0.1-3-g997b569+ff3b2f8649,22.0.1-4-g1930a60+6871d0c7f6,22.0.1-4-g5b7b756+6b209d634c,22.0.1-6-ga02864e+6871d0c7f6,22.0.1-7-g3402376+a1a2182ac4,22.0.1-7-g65f59fa+54b92689ce,master-gcc5351303a+e1b6d2281c,w.2021.32
LSST Data Management Base Package
|
Classes | |
class | CovFit |
Functions | |
def | makeCovArray (inputTuple, maxRangeFromTuple=8) |
def | symmetrize (inputArray) |
def lsst.cp.pipe.ptc.astierCovPtcFit.makeCovArray | ( | inputTuple, | |
maxRangeFromTuple = 8 |
|||
) |
Make covariances array from tuple. Parameters ---------- inputTuple: `numpy.ndarray` Structured array with rows with at least (mu, afwVar, cov, var, i, j, npix), where: mu : 0.5*(m1 + m2), where: mu1: mean value of flat1 mu2: mean value of flat2 afwVar: variance of difference flat, calculated with afw 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. maxRangeFromTuple: `int` Maximum range to select from tuple. Returns ------- cov: `numpy.array` Covariance arrays, indexed by mean signal mu. vCov: `numpy.array` Variance arrays, indexed by mean signal mu. muVals: `numpy.array` List of mean signal values. Notes ----- The input tuple should contain the following rows: (mu, cov, var, i, j, npix), with one entry per lag, and image pair. Different lags(i.e. different i and j) from the same image pair have the same values of mu1 and mu2. When i==j==0, cov = var. If the input tuple contains several video channels, one should select the data of a given channel *before* entering this routine, as well as apply(e.g.) saturation cuts. The routine returns cov[k_mu, j, i], vcov[(same indices)], and mu[k] where the first index of cov matches the one in mu. This routine implements the loss of variance due to clipping cuts when measuring variances and covariance, but this should happen inside the measurement code, where the cuts are readily available.
Definition at line 33 of file astierCovPtcFit.py.
def lsst.cp.pipe.ptc.astierCovPtcFit.symmetrize | ( | inputArray | ) |
Copy array over 4 quadrants prior to convolution. Parameters ---------- inputarray: `numpy.array` Input array to symmetrize. Returns ------- aSym: `numpy.array` Symmetrized array.
Definition at line 122 of file astierCovPtcFit.py.