lsst.cp.pipe  21.0.0-24-g07df93d+5d47c285b5
Classes | Functions
lsst.cp.pipe.ptc.astierCovPtcFit Namespace Reference

Classes

class  CovFit
 

Functions

def makeCovArray (inputTuple, maxRangeFromTuple=8)
 
def symmetrize (inputArray)
 

Function Documentation

◆ makeCovArray()

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.

◆ symmetrize()

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.