lsst.cp.pipe
21.0.0-27-gcbf119a+1a6bad3b0e
|
Classes | |
class | CovFastFourierTransform |
Functions | |
def | computeCovDirect (diffImage, weightImage, maxRange) |
def | covDirectValue (diffImage, weightImage, dx, dy) |
def | parseData (dataset) |
def | fitDataFullCovariance (dataset) |
def | getFitDataFromCovariances (i, j, mu, fullCov, fullCovModel, fullCovSqrtWeights, gain=1.0, divideByMu=False, returnMasked=False) |
def lsst.cp.pipe.ptc.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 122 of file astierCovPtcUtils.py.
def lsst.cp.pipe.ptc.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 173 of file astierCovPtcUtils.py.
def lsst.cp.pipe.ptc.astierCovPtcUtils.fitDataFullCovariance | ( | dataset | ) |
Fit data to model in Astier+19 (Eq. 20). Parameters ---------- dataset : `lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset` The dataset containing the means, (co)variances, and exposure times. Returns ------- covFitDict: `dict` Dictionary of CovFit objects, with amp names as keys. covFitNoBDict: `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 263 of file astierCovPtcUtils.py.
def lsst.cp.pipe.ptc.astierCovPtcUtils.getFitDataFromCovariances | ( | i, | |
j, | |||
mu, | |||
fullCov, | |||
fullCovModel, | |||
fullCovSqrtWeights, | |||
gain = 1.0 , |
|||
divideByMu = False , |
|||
returnMasked = False |
|||
) |
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 302 of file astierCovPtcUtils.py.
def lsst.cp.pipe.ptc.astierCovPtcUtils.parseData | ( | dataset | ) |
Returns a list of CovFit objects, indexed by amp number. Params ------ dataset : `lsst.ip.isr.ptcDataset.PhotonTransferCurveDataset` The PTC dataset containing the means, variances, and exposure times. Returns ------- covFitDict: `dict` Dictionary with amps as keys, and CovFit objects as values.
Definition at line 230 of file astierCovPtcUtils.py.