DipoleFitAlgorithm¶

class lsst.ip.diffim.DipoleFitAlgorithm(diffim, posImage=None, negImage=None)¶

Bases: object

Fit a dipole model using an image difference.

Methods Summary

`displayFitResults`(footprint, result)	Display data, model fits and residuals (currently uses matplotlib display functions).
`fitDipole`(source[, tol, rel_weight, …])	Fit a dipole model to an input `diaSource` (wraps `fitDipoleImpl`).
`fitDipoleImpl`(source[, tol, rel_weight, …])	Fit a dipole model to an input difference image.

Methods Documentation

displayFitResults(footprint, result)¶

Display data, model fits and residuals (currently uses matplotlib display functions).

Parameters:	footprint : TODO: DM-17458 Footprint containing the dipole that was fit result : `lmfit.MinimizerResult` `lmfit.MinimizerResult` object returned by `lmfit` optimizer
Returns:	fig : `matplotlib.pyplot.plot`

fitDipole(source, tol=1e-07, rel_weight=0.1, fitBackground=1, maxSepInSigma=5.0, separateNegParams=True, bgGradientOrder=1, verbose=False, display=False)¶

Fit a dipole model to an input diaSource (wraps fitDipoleImpl).

Actually, fits the subimage bounded by the input source’s footprint) and optionally constrain the fit using the pre-subtraction images self.posImage (science) and self.negImage (template). Wraps the output into a pipeBase.Struct named tuple after computing additional statistics such as orientation and SNR.

Parameters:

source : lsst.afw.table.SourceRecord

Record containing the (merged) dipole source footprint detected on the diffim

tol : float, optional

Tolerance parameter for scipy.leastsq() optimization

rel_weight : float, optional

Weighting of posImage/negImage relative to the diffim in the fit

fitBackground : int, {0, 1, 2}, optional

How to fit linear background gradient in posImage/negImage - 0: do not fit background at all - 1 (default): pre-fit the background using linear least squares and then do not fit it as part

of the dipole fitting optimization

2: pre-fit the background using linear least squares (as in 1), and use the parameter

estimates from that fit as starting parameters for an integrated “re-fit” of the background as part of the overall dipole fitting optimization.

maxSepInSigma : float, optional

Allowed window of centroid parameters relative to peak in input source footprint

separateNegParams : bool, optional

Fit separate parameters to the flux and background gradient in

bgGradientOrder : int, {0, 1, 2}, optional

Desired polynomial order of background gradient

verbose: `bool`, optional

Be verbose

display

Display input data, best fit model(s) and residuals in a matplotlib window.

Returns:

result : struct: pipeBase.Struct object containing the fit parameters and other information.
result : callable: lmfit.MinimizerResult object for debugging and error estimation, etc.

Notes

Parameter fitBackground has three options, thus it is an integer:

fitDipoleImpl(source, tol=1e-07, rel_weight=0.5, fitBackground=1, bgGradientOrder=1, maxSepInSigma=5.0, separateNegParams=True, verbose=False)¶

Fit a dipole model to an input difference image.

Actually, fits the subimage bounded by the input source’s footprint) and optionally constrain the fit using the pre-subtraction images posImage and negImage.

Parameters:

source : TODO: DM-17458: TODO: DM-17458
tol : float, optional: TODO: DM-17458
rel_weight : float, optional: TODO: DM-17458
fitBackground : int, optional: TODO: DM-17458
bgGradientOrder : int, optional: TODO: DM-17458
maxSepInSigma : float, optional: TODO: DM-17458
separateNegParams : bool, optional: TODO: DM-17458
verbose : bool, optional: TODO: DM-17458

Returns:

result : lmfit.MinimizerResult: return lmfit.MinimizerResult object containing the fit parameters and other information.

Navigation

DipoleFitAlgorithm¶