lsst.cp.pipe
21.0.0-5-gb7080ec+fcef0afafe
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Classes | |
class | CovFft |
class | LoadParams |
Functions | |
def | fftSize (s) |
def | computeCovDirect (diffImage, weightImage, maxRange) |
def | covDirectValue (diffImage, weightImage, dx, dy) |
def | loadData (tupleName, params, expIdMask) |
def | fitData (tupleName, expIdMask, r=8) |
def | getFitDataFromCovariances (i, j, mu, fullCov, fullCovModel, fullCovSqrtWeights, gain=1.0, divideByMu=False, returnMasked=False) |
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, | |
expIdMask, | |||
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. expIdMask : `dict`, [`str`, `list`] Dictionary keyed by amp names containing the masked exposure pairs. r: `int`, optional Maximum lag considered (e.g., to eliminate data beyond a separation "r": ignored in the fit). 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 310 of file astierCovPtcUtils.py.
def lsst.cp.pipe.astierCovPtcUtils.getFitDataFromCovariances | ( | i, | |
j, | |||
mu, | |||
fullCov, | |||
fullCovModel, | |||
fullCovSqrtWeights, | |||
gain = 1.0 , |
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divideByMu = False , |
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returnMasked = False |
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) |
Get measured signal and covariance, cov model, weigths, and mask at covariance lag (i, j). Parameters ---------- i : `int` Lag for covariance matrix. j: `int` Lag for covariance matrix. mu : `list` Mean signal values. fullCov: `list` of `numpy.array` Measured covariance matrices at each mean signal level in mu. fullCovSqrtWeights: `list` of `numpy.array` List of square root of measured covariances at each mean signal level in mu. fullCovModel : `list` of `numpy.array` List of modeled covariances at each mean signal level in mu. gain : `float`, optional Gain, in e-/ADU. If other than 1.0 (default), the returned quantities will be in electrons or powers of electrons. divideByMu: `bool`, optional Divide returned covariance, model, and weights by the mean signal mu? returnMasked : `bool`, optional Use mask (based on weights) in returned arrays (mu, covariance, and model)? Returns ------- mu : `numpy.array` list of signal values at (i, j). covariance : `numpy.array` Covariance at (i, j) at each mean signal mu value (fullCov[:, i, j]). covarianceModel : `numpy.array` Covariance model at (i, j). weights : `numpy.array` Weights at (i, j). maskFromWeights : `numpy.array`, optional Boolean mask of the covariance at (i,j), where the weights differ from 0. Notes ----- This function is a method of the `CovFit` class.
Definition at line 365 of file astierCovPtcUtils.py.
def lsst.cp.pipe.astierCovPtcUtils.loadData | ( | tupleName, | |
params, | |||
expIdMask | |||
) |
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. expIdMask : `dict`, [`str`, `list`] Dictionary keyed by amp names containing the masked exposure pairs. Returns ------- covFitList: `dict` Dictionary with amps as keys, and CovFit objects as values.
Definition at line 265 of file astierCovPtcUtils.py.