InsertFakesTask¶

class lsst.pipe.tasks.insertFakes.InsertFakesTask(*, config: Optional[PipelineTaskConfig] = None, log: Optional[Union[logging.Logger, LsstLogAdapter]] = None, initInputs: Optional[Dict[str, Any]] = None, **kwargs)¶

Bases: lsst.pipe.base.PipelineTask

Insert fake objects into images.

Add fake stars and galaxies to the given image, read in through the dataRef. Galaxy parameters are read in from the specified file and then modelled using galsim.

InsertFakesTask has five functions that make images of the fake sources and then add them to the image.

addPixCoords: Use the WCS information to add the pixel coordinates of each source.
mkFakeGalsimGalaxies: Use Galsim to make fake double sersic galaxies for each set of galaxy parameters in the input file.
mkFakeStars: Use the PSF information from the image to make a fake star using the magnitude information from the input file.
cleanCat: Remove rows of the input fake catalog which have half light radius, of either the bulge or the disk, that are 0. Also removes rows that have Sersic index outside of galsim’s allowed paramters. If the config option sourceSelectionColName is set then this function limits the catalog of input fakes to only those which are True in this column.
addFakeSources: Add the fake sources to the image.

Attributes Summary

canMultiprocess

Methods Summary

`addFakeSources`(image, fakeImages, sourceType)	Add the fake sources to the given image
`addPixCoords`(fakeCat, image)	Add pixel coordinates to the catalog of fakes.
`cleanCat`(fakeCat, starCheckVal)	Remove rows from the fakes catalog which have HLR = 0 for either the buldge or disk component,
`emptyMetadata`()	Empty (clear) the metadata for this Task and all sub-Tasks.
`getFullMetadata`()	Get metadata for all tasks.
`getFullName`()	Get the task name as a hierarchical name including parent task names.
`getName`()	Get the name of the task.
`getResourceConfig`()	Return resource configuration for this task.
`getTaskDict`()	Get a dictionary of all tasks as a shallow copy.
`makeField`(doc)	Make a `lsst.pex.config.ConfigurableField` for this task.
`makeSubtask`(name, **keyArgs)	Create a subtask as a new instance as the `name` attribute of this task.
`mkFakeGalsimGalaxies`(fakeCat, band, …)	Make images of fake galaxies using GalSim.
`mkFakeStars`(fakeCat, band, photoCalib, psf, …)	Make fake stars based off the properties in the fakeCat.
`processImagesForInsertion`(fakeCat, wcs, psf, …)	Process images from files into the format needed for insertion.
`run`(fakeCat, image, wcs, photoCalib)	Add fake sources to an image.
`runQuantum`(butlerQC, inputRefs, outputRefs)	Method to do butler IO and or transforms to provide in memory objects for tasks run method
`timer`(name, logLevel)	Context manager to log performance data for an arbitrary block of code.
`trimFakeCat`(fakeCat, image)	Trim the fake cat to the size of the input image plus trimBuffer padding.

Attributes Documentation

canMultiprocess = True¶

Methods Documentation

addFakeSources(image, fakeImages, sourceType)¶

Add the fake sources to the given image

Parameters:	image : `lsst.afw.image.exposure.exposure.ExposureF` The image into which the fake sources should be added fakeImages : `typing.Iterator` [`tuple` [‘lsst.afw.image.ImageF`, `lsst.geom.Point2d`]] An iterator of tuples that contains (or generates) images of fake sources, and the locations they are to be inserted at. sourceType : `str` The type (star/galaxy) of fake sources input
Returns:	image : `lsst.afw.image.exposure.exposure.ExposureF`

Notes

Uses the x, y information in the fakeCat to position an image of the fake interpolated onto the pixel grid of the image. Sets the FAKE mask plane for the pixels added with the fake source.

addPixCoords(fakeCat, image)¶

Add pixel coordinates to the catalog of fakes.

Parameters:	fakeCat : `pandas.core.frame.DataFrame` The catalog of fake sources to be input image : `lsst.afw.image.exposure.exposure.ExposureF` The image into which the fake sources should be added
Returns:	fakeCat : `pandas.core.frame.DataFrame`

cleanCat(fakeCat, starCheckVal)¶

Remove rows from the fakes catalog which have HLR = 0 for either the buldge or disk component,: also remove galaxies that have Sersic index outside the galsim min and max allowed (0.3 <= n <= 6.2).

Parameters:	fakeCat : `pandas.core.frame.DataFrame` The catalog of fake sources to be input starCheckVal : `str`, `bytes` or `int` The value that is set in the sourceType column to specifiy an object is a star.
Returns:	fakeCat : `pandas.core.frame.DataFrame` The input catalog of fake sources but with the bad objects removed

emptyMetadata() → None¶: Empty (clear) the metadata for this Task and all sub-Tasks.

getFullMetadata() → lsst.pipe.base._task_metadata.TaskMetadata¶

Get metadata for all tasks.

Returns:	metadata : `TaskMetadata` The keys are the full task name. Values are metadata for the top-level task and all subtasks, sub-subtasks, etc.

Notes

The returned metadata includes timing information (if @timer.timeMethod is used) and any metadata set by the task. The name of each item consists of the full task name with . replaced by :, followed by . and the name of the item, e.g.:

topLevelTaskName:subtaskName:subsubtaskName.itemName

using : in the full task name disambiguates the rare situation that a task has a subtask and a metadata item with the same name.

getFullName() → str¶

Get the task name as a hierarchical name including parent task names.

Returns:	fullName : `str` The full name consists of the name of the parent task and each subtask separated by periods. For example: The full name of top-level task “top” is simply “top”. The full name of subtask “sub” of top-level task “top” is “top.sub”. The full name of subtask “sub2” of subtask “sub” of top-level task “top” is “top.sub.sub2”.

getName() → str¶

Get the name of the task.

Returns:	taskName : `str` Name of the task.

See also

getFullName

getResourceConfig() → Optional[ResourceConfig]¶

Return resource configuration for this task.

Returns:	Object of type `ResourceConfig` or `None` if resource configuration is not defined for this task.

getTaskDict() → Dict[str, weakref.ReferenceType[lsst.pipe.base.task.Task]]¶

Get a dictionary of all tasks as a shallow copy.

Returns:	taskDict : `dict` Dictionary containing full task name: task object for the top-level task and all subtasks, sub-subtasks, etc.

classmethod makeField(doc: str) → lsst.pex.config.configurableField.ConfigurableField¶

Make a lsst.pex.config.ConfigurableField for this task.

Parameters:	doc : `str` Help text for the field.
Returns:	configurableField : `lsst.pex.config.ConfigurableField` A `ConfigurableField` for this task.

Examples

Provides a convenient way to specify this task is a subtask of another task.

Here is an example of use:

class OtherTaskConfig(lsst.pex.config.Config):
    aSubtask = ATaskClass.makeField("brief description of task")

makeSubtask(name: str, **keyArgs) → None¶

Create a subtask as a new instance as the name attribute of this task.

Parameters:	name : `str` Brief name of the subtask. keyArgs Extra keyword arguments used to construct the task. The following arguments are automatically provided and cannot be overridden: “config”. “parentTask”.

Notes

The subtask must be defined by Task.config.name, an instance of ConfigurableField or RegistryField.

mkFakeGalsimGalaxies(fakeCat, band, photoCalib, pixelScale, psf, image)¶

Make images of fake galaxies using GalSim.

Parameters:	band : `str` pixelScale : `float` psf : `lsst.meas.extensions.psfex.PsfexPsf` The PSF information to use to make the PSF images fakeCat : `pandas.core.frame.DataFrame` The catalog of fake sources to be input photoCalib : `lsst.afw.image.photoCalib.PhotoCalib` Photometric calibration to be used to calibrate the fake sources
Yields:	galImages : `generator` A generator of tuples of `lsst.afw.image.exposure.exposure.ExposureF` and `lsst.geom.Point2D` of their locations.

Notes

Fake galaxies are made by combining two sersic profiles, one for the bulge and one for the disk. Each component has an individual sersic index (n), a, b and position angle (PA). The combined profile is then convolved with the PSF at the specified x, y position on the image.

The names of the columns in the fakeCat are configurable and are the column names from the University of Washington simulations database as default. For more information see the doc strings attached to the config options.

See mkFakeStars doc string for an explanation of calibration to instrumental flux.

mkFakeStars(fakeCat, band, photoCalib, psf, image)¶

Make fake stars based off the properties in the fakeCat.

Parameters:

band : str
psf : lsst.meas.extensions.psfex.PsfexPsf: The PSF information to use to make the PSF images
fakeCat : pandas.core.frame.DataFrame: The catalog of fake sources to be input
image : lsst.afw.image.exposure.exposure.ExposureF: The image into which the fake sources should be added
photoCalib : lsst.afw.image.photoCalib.PhotoCalib: Photometric calibration to be used to calibrate the fake sources

Yields:

starImages : generator: A generator of tuples of lsst.afw.image.ImageF of fake stars and lsst.geom.Point2D of their locations.

Notes

To take a given magnitude and translate to the number of counts in the image we use photoCalib.magnitudeToInstFlux, which returns the instrumental flux for the given calibration radius used in the photometric calibration step. Thus calibFluxRadius should be set to this same radius so that we can normalize the PSF model to the correct instrumental flux within calibFluxRadius.

processImagesForInsertion(fakeCat, wcs, psf, photoCalib, band, pixelScale)¶

Process images from files into the format needed for insertion.

Parameters:

fakeCat : pandas.core.frame.DataFrame

The catalog of fake sources to be input

wcs : lsst.afw.geom.skyWcs.skyWcs.SkyWc

WCS to use to add fake sources

psf : lsst.meas.algorithms.CoaddPsf or

lsst.meas.extensions.psfex.PsfexPsf: The PSF information to use to make the PSF images

photoCalib : lsst.afw.image.photoCalib.PhotoCalib

Photometric calibration to be used to calibrate the fake sources

band : str

The filter band that the observation was taken in.

pixelScale : float

The pixel scale of the image the sources are to be added to.

Returns:

galImages : list: A list of tuples of lsst.afw.image.exposure.exposure.ExposureF and lsst.geom.Point2D of their locations. For sources labelled as galaxy.
starImages : list: A list of tuples of lsst.afw.image.exposure.exposure.ExposureF and lsst.geom.Point2D of their locations. For sources labelled as star.

Notes

The input fakes catalog needs to contain the absolute path to the image in the band that is being used to add images to. It also needs to have the R.A. and declination of the fake source in radians and the sourceType of the object.

run(fakeCat, image, wcs, photoCalib)¶

Add fake sources to an image.

Parameters:	fakeCat : `pandas.core.frame.DataFrame` The catalog of fake sources to be input image : `lsst.afw.image.exposure.exposure.ExposureF` The image into which the fake sources should be added wcs : `lsst.afw.geom.SkyWcs` WCS to use to add fake sources photoCalib : `lsst.afw.image.photoCalib.PhotoCalib` Photometric calibration to be used to calibrate the fake sources
Returns:	resultStruct : `lsst.pipe.base.Struct` contains : image : `lsst.afw.image.exposure.exposure.ExposureF`

Notes

Adds pixel coordinates for each source to the fakeCat and removes objects with bulge or disk half light radius = 0 (if config.doCleanCat = True).

Adds the Fake mask plane to the image which is then set by addFakeSources to mark where fake sources have been added. Uses the information in the fakeCat to make fake galaxies (using galsim) and fake stars, using the PSF models from the PSF information for the image. These are then added to the image and the image with fakes included returned.

The galsim galaxies are made using a double sersic profile, one for the bulge and one for the disk, this is then convolved with the PSF at that point.

runQuantum(butlerQC, inputRefs, outputRefs)¶

Method to do butler IO and or transforms to provide in memory objects for tasks run method

Parameters:

butlerQC : ButlerQuantumContext: A butler which is specialized to operate in the context of a lsst.daf.butler.Quantum.
inputRefs : InputQuantizedConnection: Datastructure whose attribute names are the names that identify connections defined in corresponding PipelineTaskConnections class. The values of these attributes are the lsst.daf.butler.DatasetRef objects associated with the defined input/prerequisite connections.
outputRefs : OutputQuantizedConnection: Datastructure whose attribute names are the names that identify connections defined in corresponding PipelineTaskConnections class. The values of these attributes are the lsst.daf.butler.DatasetRef objects associated with the defined output connections.

timer(name: str, logLevel: int = 10) → Iterator[None]¶

Context manager to log performance data for an arbitrary block of code.

Parameters:	name : `str` Name of code being timed; data will be logged using item name: `Start` and `End`. logLevel A `logging` level constant.

Navigation

InsertFakesTask¶