MatchBackgroundsTask

Python API summary

from lsst.pipe.tasks.matchBackgrounds import MatchBackgroundsTask
classMatchBackgroundsTask(*args, **kwargs)

Base class for data processing tasks...

attributeconfig

Access configuration fields and retargetable subtasks.

methodrun(expRefList, expDatasetType, imageScalerList=None, refExpDataRef=None, refImageScaler=None)

Match the backgrounds of a list of coadd temp exposures to a reference coadd temp exposure...

See also

See the MatchBackgroundsTask API reference for complete details.

Retargetable subtasks

No subtasks.

Configuration fields

approxWeighting

Default
True
Field type
bool Field
Use inverse-variance weighting when approximating background offset model? This will fail when the background offset is constant (this is usually only the case in testing with artificial images).(usePolynomial=True)

badMaskPlanes

Default
['NO_DATA', 'DETECTED', 'DETECTED_NEGATIVE', 'SAT', 'BAD', 'INTRP', 'CR']
Field type
str ListField
Names of mask planes to ignore while estimating the background

bestRefWeightCoverage

Default
0.4
Field type
float RangeField
Range
[0.0,1.0)
Weight given to coverage (number of pixels that overlap with patch), when calculating best reference exposure. Higher weight prefers exposures with high coverage.Ignored when reference visit is supplied

bestRefWeightLevel

Default
0.2
Field type
float RangeField
Range
[0.0,1.0)
Weight given to mean background level when calculating best reference exposure. Higher weight prefers exposures with low mean background level. Ignored when reference visit is supplied.

bestRefWeightVariance

Default
0.4
Field type
float RangeField
Range
[0.0,1.0)
Weight given to image variance when calculating best reference exposure. Higher weight prefers exposures with low image variance. Ignored when reference visit is supplied

binSize

Default
256
Field type
int Field
Bin size for gridding the difference image and fitting a spatial model

gridStatistic

Default
'MEAN'
Field type
str ChoiceField (optional)
Choices
'MEAN'
mean
'MEDIAN'
median
'MEANCLIP'
clipped mean
None
Field is optional
Type of statistic to estimate pixel value for the grid points

gridStdevEpsilon

Default
1e-08
Field type
float RangeField
Range
[0.0,inf)
Tolerance on almost zero standard deviation in a background-offset grid bin. If all bins have a standard deviation below this value, the background offset model is approximated without inverse-variance weighting. (usePolynomial=True)

interpStyle

Default
'AKIMA_SPLINE'
Field type
str ChoiceField (optional)
Choices
'CONSTANT'
Use a single constant value
'LINEAR'
Use linear interpolation
'NATURAL_SPLINE'
cubic spline with zero second derivative at endpoints
'AKIMA_SPLINE'
higher-level nonlinear spline that is more robust to outliers
'NONE'
No background estimation is to be attempted
None
Field is optional
Algorithm to interpolate the background values; ignored if usePolynomial is TrueMaps to an enum; see afw.math.Background

numIter

Default
2
Field type
int Field
Number of iterations of outlier rejection; ignored if gridStatistic != ‘MEANCLIP’.

numSigmaClip

Default
3
Field type
int Field
Sigma for outlier rejection; ignored if gridStatistic != ‘MEANCLIP’.

order

Default
8
Field type
int Field
Order of Chebyshev polynomial background model. Ignored if usePolynomial False

undersampleStyle

Default
'REDUCE_INTERP_ORDER'
Field type
str ChoiceField (optional)
Choices
'THROW_EXCEPTION'
throw an exception if there are too few points
'REDUCE_INTERP_ORDER'
use an interpolation style with a lower order.
'INCREASE_NXNYSAMPLE'
Increase the number of samples used to make the interpolation grid.
None
Field is optional
Behaviour if there are too few points in grid for requested interpolation style. Note: INCREASE_NXNYSAMPLE only allowed for usePolynomial=True.

usePolynomial

Default
False
Field type
bool Field
Fit background difference with Chebychev polynomial interpolation (using afw.math.Approximate)? If False, fit with spline interpolation using afw.math.Background