lsst.cp.pipe
20.0.0-5-g5e0f401
|
Classes | |
class | CovFft |
class | LoadParams |
Functions | |
def | fftSize (s) |
def | computeCovDirect (diffImage, weightImage, maxRange) |
def | covDirectValue (diffImage, weightImage, dx, dy) |
def | loadData (tupleName, params) |
def | fitData (tupleName, maxMu=1e9, r=8) |
def lsst.cp.pipe.astierCovPtcUtils.computeCovDirect | ( | diffImage, | |
weightImage, | |||
maxRange | |||
) |
Compute covariances of diffImage in real space. For lags larger than ~25, it is slower than the FFT way. Taken from https://github.com/PierreAstier/bfptc/ Parameters ---------- diffImage : `numpy.array` Image to compute the covariance of. weightImage : `numpy.array` Weight image of diffImage (1's and 0's for good and bad pixels, respectively). maxRange : `int` Last index of the covariance to be computed. Returns ------- outList : `list` List with tuples of the form (dx, dy, var, cov, npix), where: dx : `int` Lag in x dy : `int` Lag in y var : `float` Variance at (dx, dy). cov : `float` Covariance at (dx, dy). nPix : `int` Number of pixel pairs used to evaluate var and cov.
Definition at line 128 of file astierCovPtcUtils.py.
def lsst.cp.pipe.astierCovPtcUtils.covDirectValue | ( | diffImage, | |
weightImage, | |||
dx, | |||
dy | |||
) |
Compute covariances of diffImage in real space at lag (dx, dy). Taken from https://github.com/PierreAstier/bfptc/ (c.f., appendix of Astier+19). Parameters ---------- diffImage : `numpy.array` Image to compute the covariance of. weightImage : `numpy.array` Weight image of diffImage (1's and 0's for good and bad pixels, respectively). dx : `int` Lag in x. dy : `int` Lag in y. Returns ------- cov : `float` Covariance at (dx, dy) nPix : `int` Number of pixel pairs used to evaluate var and cov.
Definition at line 179 of file astierCovPtcUtils.py.
def lsst.cp.pipe.astierCovPtcUtils.fftSize | ( | s | ) |
Calculate the size fof one dimension for the FFT
Definition at line 122 of file astierCovPtcUtils.py.
def lsst.cp.pipe.astierCovPtcUtils.fitData | ( | tupleName, | |
maxMu = 1e9 , |
|||
r = 8 |
|||
) |
Fit data to models in Astier+19. Parameters ---------- tupleName: `numpy.recarray` Recarray with rows with at least ( mu1, mu2, cov ,var, i, j, npix), where: mu1: mean value of flat1 mu2: mean value of flat2 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. r: `int`, optional Maximum lag considered (e.g., to eliminate data beyond a separation "r": ignored in the fit). maxMu: `float`, optional Maximum signal, in ADU (e.g., to eliminate data beyond saturation). Returns ------- covFitList: `dict` Dictionary of CovFit objects, with amp names as keys. covFitNoBList: `dict` Dictionary of CovFit objects, with amp names as keys (b=0 in Eq. 20 of Astier+19). Notes ----- The parameters of the full model for C_ij(mu) ("C_ij" and "mu" in ADU^2 and ADU, respectively) in Astier+19 (Eq. 20) are: "a" coefficients (r by r matrix), units: 1/e "b" coefficients (r by r matrix), units: 1/e noise matrix (r by r matrix), units: e^2 gain, units: e/ADU "b" appears in Eq. 20 only through the "ab" combination, which is defined in this code as "c=ab".
Definition at line 323 of file astierCovPtcUtils.py.
def lsst.cp.pipe.astierCovPtcUtils.loadData | ( | tupleName, | |
params | |||
) |
Returns a list of CovFit objects, indexed by amp number. Params ------ tupleName: `numpy.recarray` Recarray with rows with at least ( mu1, mu2, cov ,var, i, j, npix), where: mu1: mean value of flat1 mu2: mean value of flat2 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. params: `covAstierptcUtil.LoadParams` Object with values to drive the bahaviour of fits. Returns ------- covFitList: `dict` Dictionary with amps as keys, and CovFit objects as values.
Definition at line 273 of file astierCovPtcUtils.py.