EstimateZernikesLatissTask¶

class lsst.ts.wep.task.EstimateZernikesLatissTask(**kwargs)¶

Bases: EstimateZernikesBaseTask

Run Zernike Estimation for Latiss (AuxTel)

Attributes Summary

canMultiprocess

Methods Summary

`assignExtraIntraIdx`(focusZVal0, focusZVal1)	Identify which exposure in the list is the extra-focal and which is the intra-focal based upon `FOCUSZ` parameter in header.
`calcBlendOffsets`(donutStamp, eulerAngle)	Calculate the offsets between the center of the donutStamp image and the centers of blended donuts appearing on the stamp image.
`calculateFinalCentroid`(exposure, template, ...)	Recentroid donut from catalog values by convolving with template.
`cutOutStamps`(exposure, donutCatalog, ...)	Cut out postage stamps for sources in catalog.
`emptyMetadata`()	Empty (clear) the metadata for this Task and all sub-Tasks.
`estimateZernikes`(donutStampsExtra, ...)	Take the donut postage stamps and estimate the Zernike coefficients.
`getCombinedZernikes`(zernikeArray)	Combine the Zernike coefficients from stamp pairs on the CCD to create one final value for the CCD.
`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.
`getTemplate`(detectorName, defocalType, ...)	Get the templates for the detector.
`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.
`run`(exposures, donutCatalog, camera)	Run task algorithm on in-memory data.
`runQuantum`(butlerQC, inputRefs, outputRefs)	Method to do butler IO and or transforms to provide in memory objects for tasks run method
`shiftCenter`(center, boundary, distance)	Shift the center if its distance to boundary is less than required.
`timer`(name[, logLevel])	Context manager to log performance data for an arbitrary block of code.

Attributes Documentation

canMultiprocess: ClassVar[bool] = True¶

Methods Documentation

assignExtraIntraIdx(focusZVal0, focusZVal1)¶

Identify which exposure in the list is the extra-focal and which is the intra-focal based upon FOCUSZ parameter in header. For Latiss, the negative value is extra-focal (z-axis points down towards M1 mirror): — extra –0– intra —-> (+z)

If both are negative, we assume that the one more negative is extra-focal: — extra —– intra –0–> (+z)

Parameters:

focusZVal0float: The FOCUSZ parameter from the first exposure.
focusZVal1float: The FOCUSZ parameter from the second exposure.

Returns:

int: Index in list which is extra-focal image.
int: Index in list which is intra-focal image.

Raises:

ValueError: Exposures must be a pair with one intra-focal and one extra-focal image.

calcBlendOffsets(donutStamp, eulerAngle)¶

Calculate the offsets between the center of the donutStamp image and the centers of blended donuts appearing on the stamp image. Include rotations for rotated wavefront sensors.

Parameters:

donutStampDonutStamp: Extra or intra-focal DonutStamp object.
eulerAnglefloat: Angle of rotation of sensor compared to the standard alignment of the focal plane.

Returns:

numpy.ndarray: Offsets of blended donuts compared to center of DonutStamp postage stamp image.

calculateFinalCentroid(exposure, template, xCent, yCent)¶

Recentroid donut from catalog values by convolving with template. Also return the appropriate corner values for the final donutStamp taking into account donut possibly being near the edges of the exposure and compensating appropriately.

Parameters:

exposurelsst.afw.image.Exposure: Exposure with the donut image.
templatenumpy ndarray: Donut template for the exposure.
xCentint: X pixel donut center from donutCatalog.
yCentint: Y pixel donut center from donutCatalog.

Returns:

int: Final donut x centroid pixel position on exposure.
int: Final donut y centroid pixel position on exposure.
int: Final x corner position on exposure for donutStamp BBox.
int: Final y corner position on exposure for donutStamp BBox.

cutOutStamps(exposure, donutCatalog, defocalType, cameraName)¶

Cut out postage stamps for sources in catalog.

Parameters:

exposurelsst.afw.image.Exposure: Post-ISR image with defocal donuts sources.
donutCatalogpandas DataFrame: Source catalog for the pointing.
defocalTypeenum ‘DefocalType’: Defocal type of the donut image.
cameraNamestr: Name of camera for the exposure. Can accept “LSSTCam”, “LSSTComCam”, “LATISS”.

Returns:

DonutStamps: Collection of postage stamps as lsst.afw.image.maskedImage.MaskedImage with additional metadata.

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

estimateZernikes(donutStampsExtra, donutStampsIntra, cameraName, detectorType)¶

Take the donut postage stamps and estimate the Zernike coefficients.

Parameters:

donutStampsExtraDonutStamps: Extra-focal donut postage stamps.
donutStampsIntraDonutStamps: Intra-focal donut postage stamps.
cameraNamestr: Name of camera for the exposure. Can accept “LSSTCam”, “LSSTComCam”, “LATISS”.
detectorTypelsst.afw.cameraGeom.DetectorType: Type of CCD. “SCIENCE” or “WAVEFRONT”.

Returns:

numpy.ndarray: Zernike coefficients for the exposure. Will return one set of coefficients per set of stamps, not one set of coefficients per detector so this will be a 2-D numpy array with the number of rows equal to the number of donut stamps and the number of columns equal to the number of Zernike coefficients.

getCombinedZernikes(zernikeArray)¶

Combine the Zernike coefficients from stamp pairs on the CCD to create one final value for the CCD.

Parameters:

zernikeArraynumpy ndarray: The full set of zernike coefficients for each pair of donuts on the CCD. Each row of the array should be the set of Zernike coefficients for a single donut pair.

Returns:

structlsst.pipe.base.Struct

The struct contains the following data:

combinedZernikesnumpy.ndarray
The final combined Zernike coefficients from the CCD.
combineFlagsnumpy.ndarray
Flag indicating a particular set of Zernike coefficients was not used in the final estimate. If the values in a row in the zernikeArray were used then its index is 0. A value of 1 means the coefficients from that row in the input zernikeArray were not used.

getFullMetadata() → TaskMetadata¶

Get metadata for all tasks.

Returns:

metadataTaskMetadata: 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:

fullNamestr

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:

taskNamestr: 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, ReferenceType[Task]]¶

Get a dictionary of all tasks as a shallow copy.

Returns:

taskDictdict: Dictionary containing full task name: task object for the top-level task and all subtasks, sub-subtasks, etc.

getTemplate(detectorName, defocalType, donutTemplateSize, camType=CamType.LsstCam, opticalModel='offAxis', pixelScale=0.2)¶

Get the templates for the detector.

Parameters:

detectorNamestr: Name of the CCD (e.g. ‘R22_S11’).
defocalTypeenum ‘DefocalType’: Defocal type of the donut image.
donutTemplateSizeint: Size of Template in pixels
camTypeenum ‘CamType’, optional: Camera type. (The default is CamType.LsstCam)
opticalModelstr, optional: Optical model. It can be “paraxial”, “onAxis”, or “offAxis”. (The default is “offAxis”)
pixelScalefloat, optional: The pixels to arcseconds conversion factor. (The default is 0.2)

Returns:

numpy.ndarray: Template donut for the detector and defocal type.

classmethod makeField(doc: str) → ConfigurableField¶

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

Parameters:

docstr: Help text for the field.

Returns:

configurableFieldlsst.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: Any) → None¶

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

Parameters:

namestr

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.

run(exposures: List[Exposure], donutCatalog: List[DataFrame], camera: Camera) → Struct¶

Run task algorithm on in-memory data.

This method should be implemented in a subclass. This method will receive keyword arguments whose names will be the same as names of connection fields describing input dataset types. Argument values will be data objects retrieved from data butler. If a dataset type is configured with multiple field set to True then the argument value will be a list of objects, otherwise it will be a single object.

If the task needs to know its input or output DataIds then it has to override runQuantum method instead.

This method should return a Struct whose attributes share the same name as the connection fields describing output dataset types.

Returns:

structStruct: Struct with attribute names corresponding to output connection fields

Examples

Typical implementation of this method may look like:

def run(self, input, calib):
    # "input", "calib", and "output" are the names of the config
    # fields

    # Assuming that input/calib datasets are `scalar` they are
    # simple objects, do something with inputs and calibs, produce
    # output image.
    image = self.makeImage(input, calib)

    # If output dataset is `scalar` then return object, not list
    return Struct(output=image)

runQuantum(butlerQC: ButlerQuantumContext, inputRefs: InputQuantizedConnection, outputRefs: OutputQuantizedConnection) → None¶

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

Parameters:

butlerQCButlerQuantumContext: A butler which is specialized to operate in the context of a lsst.daf.butler.Quantum.
inputRefsInputQuantizedConnection: 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.
outputRefsOutputQuantizedConnection: 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.

shiftCenter(center, boundary, distance)¶

Shift the center if its distance to boundary is less than required.

Parameters:

centerfloat: Center point.
boundaryfloat: Boundary point.
distancefloat: Required distance.

Returns:

float: Shifted center.

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

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

Parameters:

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

Navigation

EstimateZernikesLatissTask¶