BrighterFatterKernel

class lsst.ip.isr.BrighterFatterKernel(camera=None, level=None, **kwargs)

Bases: lsst.ip.isr.IsrCalib

Calibration of brighter-fatter kernels for an instrument.

ampKernels are the kernels for each amplifier in a detector, as generated by having level == 'AMP'.

detectorKernel is the kernel generated for a detector as a whole, as generated by having level == 'DETECTOR'.

makeDetectorKernelFromAmpwiseKernels is a method to generate the kernel for a detector, constructed by averaging together the ampwise kernels in the detector. The existing application code is only defined for kernels with level == 'DETECTOR', so this method is used if the supplied kernel was built with level == 'AMP'.

Parameters:

camera : lsst.afw.cameraGeom.Camera

Camera describing detector geometry.

level : str

Level the kernels will be generated for.

log : logging.Logger, optional

Log to write messages to.

**kwargs

Parameters to pass to parent constructor.

Notes

Version 1.1 adds the expIdMask property, and substitutes means and variances for rawMeans and rawVariances from the PTC dataset.

expIdMask : dict, [str,`numpy.ndarray`]

Dictionary keyed by amp names containing the mask produced after outlier rejection.

rawMeans : dict, [str, numpy.ndarray]

Dictionary keyed by amp names containing the unmasked average of the means of the exposures in each flat pair.

rawVariances : dict, [str, numpy.ndarray]

Dictionary keyed by amp names containing the variance of the difference image of the exposures in each flat pair. Corresponds to rawVars of PTC.

rawXcorrs : dict, [str, numpy.ndarray]

Dictionary keyed by amp names containing an array of measured covariances per mean flux. Corresponds to covariances of PTC.

badAmps : list

List of bad amplifiers names.

shape : tuple

Tuple of the shape of the BFK kernels.

gain : dict, [str,`float`]

Dictionary keyed by amp names containing the fitted gains.

noise : dict, [str,`float`]

Dictionary keyed by amp names containing the fitted noise.

meanXcorrs : dict, [str,`numpy.ndarray`]

Dictionary keyed by amp names containing the averaged cross-correlations.

valid : dict, [str,`bool`]

Dictionary keyed by amp names containing validity of data.

ampKernels : dict, [str, numpy.ndarray]

Dictionary keyed by amp names containing the BF kernels.

detKernels : dict

Dictionary keyed by detector names containing the BF kernels.

Attributes Summary

requiredAttributes

Methods Summary

apply(target)	Method to apply the calibration to the target object.
calibInfoFromDict(dictionary)	Handle common keywords.
determineCalibClass(metadata, message)	Attempt to find calibration class in metadata.
fromDetector(detector)	Modify the calibration parameters to match the supplied detector.
fromDict(dictionary)	Construct a calibration from a dictionary of properties.
fromTable(tableList)	Construct calibration from a list of tables.
getLengths()	Return the set of lengths needed for reshaping components.
getMetadata()	Retrieve metadata associated with this calibration.
initFromCamera(camera[, detectorId])	Initialize kernel structure from camera.
makeDetectorKernelFromAmpwiseKernels(…[, …])	Average the amplifier level kernels to create a detector level kernel.
readFits(filename, **kwargs)	Read calibration data from a FITS file.
readText(filename, **kwargs)	Read calibration representation from a yaml/ecsv file.
replaceDetectorKernelWithAmpKernel(ampName, …)
setMetadata(metadata)	Store a copy of the supplied metadata with this calibration.
toDict()	Return a dictionary containing the calibration properties.
toTable()	Construct a list of tables containing the information in this calibration.
updateMetadata([setDate])	Update calibration metadata.
validate([other])	Validate that this calibration is defined and can be used.
writeFits(filename)	Write calibration data to a FITS file.
writeText(filename[, format])	Write the calibration data to a text file.

Attributes Documentation

requiredAttributes

Methods Documentation

apply(target)

Method to apply the calibration to the target object.

Parameters:

target : object

Thing to validate against.

Returns:

valid : bool

Returns true if the calibration was applied correctly.

Raises:

NotImplementedError

Raised if not implemented.

calibInfoFromDict(dictionary)

Handle common keywords.

This isn’t an ideal solution, but until all calibrations expect to find everything in the metadata, they still need to search through dictionaries.

Parameters:

dictionary : dict or lsst.daf.base.PropertyList

Source for the common keywords.

Raises:

RuntimeError

Raised if the dictionary does not match the expected OBSTYPE.

classmethod determineCalibClass(metadata, message)

Attempt to find calibration class in metadata.

Parameters:

metadata : dict or lsst.daf.base.PropertyList

Metadata possibly containing a calibration class entry.

message : str

Message to include in any errors.

Returns:

calibClass : object

The class to use to read the file contents. Should be an lsst.ip.isr.IsrCalib subclass.

Raises:

ValueError

Raised if the resulting calibClass is the base lsst.ip.isr.IsrClass (which does not implement the content methods).

fromDetector(detector)

Modify the calibration parameters to match the supplied detector.

Parameters:

detector : lsst.afw.cameraGeom.Detector

Detector to use to set parameters from.

Raises:

NotImplementedError

Raised if not implemented by a subclass. This needs to be implemented by subclasses for each calibration type.

classmethod fromDict(dictionary)

Construct a calibration from a dictionary of properties.

Parameters:

dictionary : dict

Dictionary of properties.

Returns:

calib : lsst.ip.isr.BrighterFatterKernel

Constructed calibration.

Raises:

RuntimeError

Raised if the supplied dictionary is for a different calibration. Raised if the version of the supplied dictionary is 1.0.

classmethod fromTable(tableList)

Construct calibration from a list of tables.

This method uses the fromDict method to create the calibration, after constructing an appropriate dictionary from the input tables.

Parameters:

tableList : list [astropy.table.Table]

List of tables to use to construct the brighter-fatter calibration.

Returns:

calib : lsst.ip.isr.BrighterFatterKernel

The calibration defined in the tables.

getLengths()

Return the set of lengths needed for reshaping components.

Returns:

kernelLength : int

Product of the elements of self.shape.

smallLength : int

Size of an untiled covariance.

nObs : int

Number of observation pairs used in the kernel.

getMetadata()

Retrieve metadata associated with this calibration.

Returns:

meta : lsst.daf.base.PropertyList

Metadata. The returned PropertyList can be modified by the caller and the changes will be written to external files.

initFromCamera(camera, detectorId=None)

Initialize kernel structure from camera.

Parameters:

camera : lsst.afw.cameraGeom.Camera

Camera to use to define geometry.

detectorId : int, optional

Index of the detector to generate.

Returns:

calib : lsst.ip.isr.BrighterFatterKernel

The initialized calibration.

Raises:

RuntimeError

Raised if no detectorId is supplied for a calibration with level='AMP'.

makeDetectorKernelFromAmpwiseKernels(detectorName, ampsToExclude=[])

Average the amplifier level kernels to create a detector level kernel.

classmethod readFits(filename, **kwargs)

Read calibration data from a FITS file.

Parameters:

filename : str

Filename to read data from.

kwargs : dict or collections.abc.Mapping`, optional

Set of key=value pairs to pass to the fromTable method.

Returns:

calib : lsst.ip.isr.IsrCalib

Calibration contained within the file.

classmethod readText(filename, **kwargs)

Read calibration representation from a yaml/ecsv file.

Parameters:

filename : str

Name of the file containing the calibration definition.

kwargs : dict or collections.abc.Mapping`, optional

Set of key=value pairs to pass to the fromDict or fromTable methods.

Returns:

calib : IsrCalibType

Calibration class.

Raises:

RuntimeError

Raised if the filename does not end in “.ecsv” or “.yaml”.

replaceDetectorKernelWithAmpKernel(ampName, detectorName)

setMetadata(metadata)

Store a copy of the supplied metadata with this calibration.

Parameters:

metadata : lsst.daf.base.PropertyList

Metadata to associate with the calibration. Will be copied and overwrite existing metadata.

toDict()

Return a dictionary containing the calibration properties.

The dictionary should be able to be round-tripped through fromDict.

Returns:

dictionary : dict

Dictionary of properties.

toTable()

Construct a list of tables containing the information in this calibration.

The list of tables should create an identical calibration after being passed to this class’s fromTable method.

Returns:

tableList : list [lsst.afw.table.Table]

List of tables containing the crosstalk calibration information.

updateMetadata(setDate=False, **kwargs)

Update calibration metadata.

This calls the base class’s method after ensuring the required calibration keywords will be saved.

Parameters:

setDate : bool, optional

Update the CALIBDATE fields in the metadata to the current time. Defaults to False.

kwargs

Other keyword parameters to set in the metadata.

validate(other=None)

Validate that this calibration is defined and can be used.

Parameters:

other : object, optional

Thing to validate against.

Returns:

valid : bool

Returns true if the calibration is valid and appropriate.

writeFits(filename)

Write calibration data to a FITS file.

Parameters:

filename : str

Filename to write data to.

Returns:

used : str

The name of the file used to write the data.

writeText(filename, format='auto')

Write the calibration data to a text file.

Parameters:

filename : str

Name of the file to write.

format : str

Format to write the file as. Supported values are:

"auto" : Determine filetype from filename. "yaml" : Write as yaml. "ecsv" : Write as ecsv.

Returns:

used : str

The name of the file used to write the data. This may differ from the input if the format is explicitly chosen.

Raises:

RuntimeError

Raised if filename does not end in a known extension, or if all information cannot be written.

Notes

The file is written to YAML/ECSV format and will include any associated metadata.