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