AlardLuptonSubtractConfig

class lsst.ip.diffim.AlardLuptonSubtractConfig(*args, **kw)

Bases: AlardLuptonSubtractBaseConfig, PipelineTaskConfig

Attributes Summary

allowKernelSourceDetection

Re-run source detection for kernel candidates if an error is encountered while calculating the matching kernel.

badMaskPlanes

Mask planes to interpolate over.

badSourceFlags

Flags that, if set, the associated source should not be used to determine the PSF matching kernel.

connections

Field which refers to a dynamically added configuration class which is based on a PipelineTaskConnections class.

decorrelate

Task to decorrelate the image difference.

detectionThreshold

Minimum signal to noise ratio of detected sources to use for calculating the PSF matching kernel.

detectionThresholdMax

Maximum signal to noise ratio of detected sources to use for calculating the PSF matching kernel.

doApplyExternalCalibrations

Replace science Exposure's calibration objects with those in visitSummary.

doApplyFinalizedPsf

Replace science Exposure's psf and aperture correction map with those in finalizedPsfApCorrCatalog.

doDecorrelation

Perform diffim decorrelation to undo pixel correlation due to A&L kernel convolution? If True, also update the diffim PSF.

doScaleVariance

Scale variance of the image difference? (bool, default True)

doSubtractBackground

Subtract the background fit when solving the kernel? (bool, default True)

excludeMaskPlanes

Mask planes to exclude when selecting sources for PSF matching.

history

makeKernel

Task to construct a matching kernel for convolution.

maxKernelSources

Maximum number of sources to use for calculating the PSF matching kernel.Set to -1 to disable.

minKernelSources

Minimum number of sources needed for calculating the PSF matching kernel.

minTemplateFractionForExpectedSuccess

Raise NoWorkFound if PSF-matching fails and template covers less than this fraction of pixels.

mode

Choose which image to convolve at runtime, or require that a specific image is convolved.

preserveTemplateMask

Mask planes from the template to propagate to the image difference.

renameTemplateMask

Mask planes from the template to propagate to the image differencewith '_TEMPLATE' appended to the name.

requiredTemplateFraction

Raise NoWorkFound and do not attempt image subtraction if template covers less than this fraction of pixels.

saveLogOutput

Flag to enable/disable saving of log output for a task, enabled by default.

scaleVariance

Subtask to rescale the variance of the template to the statistically expected level.

Methods Summary

applyConfigOverrides(instrument, ...)

Apply config overrides to this config instance.

compare(other[, shortcut, rtol, atol, output])

Compare this configuration to another Config for equality.

formatHistory(name, **kwargs)

Format a configuration field's history to a human-readable format.

freeze()

Make this config, and all subconfigs, read-only.

items()

Get configurations as (field name, field value) pairs.

keys()

Get field names.

load(filename[, root])

Modify this config in place by executing the Python code in a configuration file.

loadFromStream(stream[, root, filename, ...])

Modify this Config in place by executing the Python code in the provided stream.

loadFromString(code[, root, filename, ...])

Modify this Config in place by executing the Python code in the provided string.

names()

Get all the field names in the config, recursively.

save(filename[, root])

Save a Python script to the named file, which, when loaded, reproduces this config.

saveToStream(outfile[, root, skipImports])

Save a configuration file to a stream, which, when loaded, reproduces this config.

saveToString([skipImports])

Return the Python script form of this configuration as an executable string.

setDefaults()

Subclass hook for computing defaults.

toDict()

Make a dictionary of field names and their values.

update(**kw)

Update values of fields specified by the keyword arguments.

validate()

Validate the Config, raising an exception if invalid.

values()

Get field values.

Attributes Documentation

allowKernelSourceDetection

Re-run source detection for kernel candidates if an error is encountered while calculating the matching kernel. (bool, default False)

badMaskPlanes

Mask planes to interpolate over. (List, default ('NO_DATA', 'BAD', 'SAT', 'EDGE'))

badSourceFlags

Flags that, if set, the associated source should not be used to determine the PSF matching kernel. (List, default ('sky_source', 'slot_Centroid_flag', 'slot_ApFlux_flag', 'slot_PsfFlux_flag', 'base_PixelFlags_flag_interpolated', 'base_PixelFlags_flag_saturated', 'base_PixelFlags_flag_bad'))

connections: pexConfig.ConfigField

Field which refers to a dynamically added configuration class which is based on a PipelineTaskConnections class.

decorrelate

Task to decorrelate the image difference. (ConfigurableInstance, default <class 'lsst.ip.diffim.imageDecorrelation.DecorrelateALKernelConfig'>)

detectionThreshold

Minimum signal to noise ratio of detected sources to use for calculating the PSF matching kernel. (float, default 10)

detectionThresholdMax

Maximum signal to noise ratio of detected sources to use for calculating the PSF matching kernel. (float, default 500)

doApplyExternalCalibrations

Replace science Exposure’s calibration objects with those in visitSummary. Ignored if doApplyFinalizedPsf is True. (`bool, default False)

doApplyFinalizedPsf

Replace science Exposure’s psf and aperture correction map with those in finalizedPsfApCorrCatalog. Deprecated: Deprecated in favor of doApplyExternalCalibrations. Will be removed after v26. (bool, default False)

doDecorrelation

Perform diffim decorrelation to undo pixel correlation due to A&L kernel convolution? If True, also update the diffim PSF. (bool, default True)

doScaleVariance

Scale variance of the image difference? (bool, default True)

doSubtractBackground

Subtract the background fit when solving the kernel? (bool, default True)

excludeMaskPlanes

Mask planes to exclude when selecting sources for PSF matching. (List, default ('NO_DATA', 'BAD', 'SAT', 'EDGE', 'FAKE'))

history

Read-only history.

makeKernel

Task to construct a matching kernel for convolution. (ConfigurableInstance, default <class 'lsst.ip.diffim.makeKernel.MakeKernelConfig'>)

maxKernelSources

Maximum number of sources to use for calculating the PSF matching kernel.Set to -1 to disable. (int, default 1000)

minKernelSources

Minimum number of sources needed for calculating the PSF matching kernel. (int, default 3)

minTemplateFractionForExpectedSuccess

Raise NoWorkFound if PSF-matching fails and template covers less than this fraction of pixels. If the fraction of pixels covered by the template is less than this value (and greater than requiredTemplateFraction) this task is attempted but failure is anticipated and tolerated. (float, default 0.2)

mode

Choose which image to convolve at runtime, or require that a specific image is convolved. (str, default 'convolveTemplate')

Allowed values:

'auto'

Choose which image to convolve at runtime.

'convolveScience'

Only convolve the science image.

'convolveTemplate'

Only convolve the template image.

'None'

Field is optional

preserveTemplateMask

Mask planes from the template to propagate to the image difference. (List, default ('NO_DATA', 'BAD'))

renameTemplateMask

Mask planes from the template to propagate to the image differencewith ‘_TEMPLATE’ appended to the name. (List, default ('SAT', 'INJECTED', 'INJECTED_CORE'))

requiredTemplateFraction

Raise NoWorkFound and do not attempt image subtraction if template covers less than this fraction of pixels. Setting to 0 will always attempt image subtraction. (float, default 0.1)

saveLogOutput

Flag to enable/disable saving of log output for a task, enabled by default. (bool, default True)

scaleVariance

Subtask to rescale the variance of the template to the statistically expected level. (ConfigurableInstance, default <class 'lsst.meas.algorithms.scaleVariance.ScaleVarianceConfig'>)

Methods Documentation

applyConfigOverrides(instrument: Instrument | None, taskDefaultName: str, pipelineConfigs: Iterable[ConfigIR] | None, parameters: ParametersIR, label: str) None

Apply config overrides to this config instance.

Parameters:
instrumentInstrument or None

An instance of the Instrument specified in a pipeline. If None then the pipeline did not specify and instrument.

taskDefaultNamestr

The default name associated with the Task class. This may be used with instrumental overrides.

pipelineConfigsIterable of ConfigIR

An iterable of ConfigIR objects that contain overrides to apply to this config instance.

parametersParametersIR

Parameters defined in a Pipeline which are used in formatting of config values across multiple Task in a pipeline.

labelstr

The label associated with this class’s Task in a pipeline.

compare(other, shortcut=True, rtol=1e-08, atol=1e-08, output=None)

Compare this configuration to another Config for equality.

Parameters:
otherlsst.pex.config.Config

Other Config object to compare against this config.

shortcutbool, optional

If True, return as soon as an inequality is found. Default is True.

rtolfloat, optional

Relative tolerance for floating point comparisons.

atolfloat, optional

Absolute tolerance for floating point comparisons.

outputcallable, optional

A callable that takes a string, used (possibly repeatedly) to report inequalities.

Returns:
isEqualbool

True when the two lsst.pex.config.Config instances are equal. False if there is an inequality.

Notes

Unselected targets of RegistryField fields and unselected choices of ConfigChoiceField fields are not considered by this method.

Floating point comparisons are performed by numpy.allclose.

formatHistory(name, **kwargs)

Format a configuration field’s history to a human-readable format.

Parameters:
namestr

Name of a Field in this config.

**kwargs

Keyword arguments passed to lsst.pex.config.history.format.

Returns:
historystr

A string containing the formatted history.

freeze()

Make this config, and all subconfigs, read-only.

items()

Get configurations as (field name, field value) pairs.

Returns:
itemsItemsView

Iterator of tuples for each configuration. Tuple items are:

  1. Field name.

  2. Field value.

keys()

Get field names.

Returns:
namesKeysView

List of lsst.pex.config.Field names.

load(filename, root='config')

Modify this config in place by executing the Python code in a configuration file.

Parameters:
filenamestr

Name of the configuration file. A configuration file is Python module.

rootstr, optional

Name of the variable in file that refers to the config being overridden.

For example, the value of root is "config" and the file contains:

config.myField = 5

Then this config’s field myField is set to 5.

loadFromStream(stream, root='config', filename=None, extraLocals=None)

Modify this Config in place by executing the Python code in the provided stream.

Parameters:
streamfile-like object, str, bytes, or CodeType

Stream containing configuration override code. If this is a code object, it should be compiled with mode="exec".

rootstr, optional

Name of the variable in file that refers to the config being overridden.

For example, the value of root is "config" and the file contains:

config.myField = 5

Then this config’s field myField is set to 5.

filenamestr, optional

Name of the configuration file, or None if unknown or contained in the stream. Used for error reporting.

extraLocalsdict of str to object, optional

Any extra variables to include in local scope when loading.

Notes

For backwards compatibility reasons, this method accepts strings, bytes and code objects as well as file-like objects. New code should use loadFromString instead for most of these types.

loadFromString(code, root='config', filename=None, extraLocals=None)

Modify this Config in place by executing the Python code in the provided string.

Parameters:
codestr, bytes, or CodeType

Stream containing configuration override code.

rootstr, optional

Name of the variable in file that refers to the config being overridden.

For example, the value of root is "config" and the file contains:

config.myField = 5

Then this config’s field myField is set to 5.

filenamestr, optional

Name of the configuration file, or None if unknown or contained in the stream. Used for error reporting.

extraLocalsdict of str to object, optional

Any extra variables to include in local scope when loading.

Raises:
ValueError

Raised if a key in extraLocals is the same value as the value of the root argument.

names()

Get all the field names in the config, recursively.

Returns:
nameslist of str

Field names.

save(filename, root='config')

Save a Python script to the named file, which, when loaded, reproduces this config.

Parameters:
filenamestr

Desination filename of this configuration.

rootstr, optional

Name to use for the root config variable. The same value must be used when loading (see lsst.pex.config.Config.load).

saveToStream(outfile, root='config', skipImports=False)

Save a configuration file to a stream, which, when loaded, reproduces this config.

Parameters:
outfilefile-like object

Destination file object write the config into. Accepts strings not bytes.

rootstr, optional

Name to use for the root config variable. The same value must be used when loading (see lsst.pex.config.Config.load).

skipImportsbool, optional

If True then do not include import statements in output, this is to support human-oriented output from pipetask where additional clutter is not useful.

saveToString(skipImports=False)

Return the Python script form of this configuration as an executable string.

Parameters:
skipImportsbool, optional

If True then do not include import statements in output, this is to support human-oriented output from pipetask where additional clutter is not useful.

Returns:
codestr

A code string readable by loadFromString.

setDefaults()

Subclass hook for computing defaults.

Notes

Derived Config classes that must compute defaults rather than using the Field instances’s defaults should do so here. To correctly use inherited defaults, implementations of setDefaults must call their base class’s setDefaults.

toDict()

Make a dictionary of field names and their values.

Returns:
dict_dict

Dictionary with keys that are Field names. Values are Field values.

Notes

This method uses the toDict method of individual fields. Subclasses of Field may need to implement a toDict method for this method to work.

update(**kw)

Update values of fields specified by the keyword arguments.

Parameters:
**kw

Keywords are configuration field names. Values are configuration field values.

Notes

The __at and __label keyword arguments are special internal keywords. They are used to strip out any internal steps from the history tracebacks of the config. Do not modify these keywords to subvert a Config instance’s history.

Examples

This is a config with three fields:

>>> from lsst.pex.config import Config, Field
>>> class DemoConfig(Config):
...     fieldA = Field(doc='Field A', dtype=int, default=42)
...     fieldB = Field(doc='Field B', dtype=bool, default=True)
...     fieldC = Field(doc='Field C', dtype=str, default='Hello world')
...
>>> config = DemoConfig()

These are the default values of each field:

>>> for name, value in config.iteritems():
...     print(f"{name}: {value}")
...
fieldA: 42
fieldB: True
fieldC: 'Hello world'

Using this method to update fieldA and fieldC:

>>> config.update(fieldA=13, fieldC='Updated!')

Now the values of each field are:

>>> for name, value in config.iteritems():
...     print(f"{name}: {value}")
...
fieldA: 13
fieldB: True
fieldC: 'Updated!'
validate()

Validate the Config, raising an exception if invalid.

Raises:
lsst.pex.config.FieldValidationError

Raised if verification fails.

Notes

The base class implementation performs type checks on all fields by calling their validate methods.

Complex single-field validation can be defined by deriving new Field types. For convenience, some derived lsst.pex.config.Field-types (ConfigField and ConfigChoiceField) are defined in lsst.pex.config that handle recursing into subconfigs.

Inter-field relationships should only be checked in derived Config classes after calling this method, and base validation is complete.

values()

Get field values.

Returns:
valuesValuesView

Iterator of field values.