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 | 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.