CalcZernikesTask¶
- class lsst.ts.wep.task.CalcZernikesTask(**kwargs)¶
Bases:
PipelineTaskRun Zernike Estimation on corner wavefront sensors (CWFS)
Attributes Summary
Methods Summary
calcBlendOffsets(donutStamp, eulerAngle)Calculate the offsets between the center of the donutStamp image and the centers of blended donuts appearing on the stamp image.
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.
Get metadata for all tasks.
Get the task name as a hierarchical name including parent task names.
getName()Get the name of the task.
Get a dictionary of all tasks as a shallow copy.
makeField(doc)Make a
lsst.pex.config.ConfigurableFieldfor this task.makeSubtask(name, **keyArgs)Create a subtask as a new instance as the
nameattribute of this task.run(donutStampsExtra, donutStampsIntra)Run task algorithm on in-memory data.
runQuantum(butlerQC, inputRefs, outputRefs)Do butler IO and transform to provide in memory objects for tasks
runmethod.timer(name[, logLevel])Context manager to log performance data for an arbitrary block of code.
Attributes Documentation
Methods Documentation
- 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.
- estimateZernikes(donutStampsExtra, donutStampsIntra)¶
Take the donut postage stamps and estimate the Zernike coefficients.
- Parameters:
- donutStampsExtraDonutStamps
Extra-focal donut postage stamps.
- donutStampsIntraDonutStamps
Intra-focal donut postage stamps.
- 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:
- struct
lsst.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
zernikeArraywere used then its index is 0. A value of 1 means the coefficients from that row in the inputzernikeArraywere not used.
- struct
- getFullMetadata() 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.
- metadata
Notes
The returned metadata includes timing information (if
@timer.timeMethodis 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”.
- fullName
- 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.
- taskDict
- classmethod makeField(doc: str) ConfigurableField¶
Make a
lsst.pex.config.ConfigurableFieldfor this task.- Parameters:
- doc
str Help text for the field.
- doc
- Returns:
- configurableField
lsst.pex.config.ConfigurableField A
ConfigurableFieldfor this task.
- configurableField
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
nameattribute 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.
- name
Notes
The subtask must be defined by
Task.config.name, an instance ofConfigurableFieldorRegistryField.
- run(donutStampsExtra: DonutStamps, donutStampsIntra: DonutStamps) 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
multiplefield set toTruethen 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
runQuantummethod instead.This method should return a
Structwhose attributes share the same name as the connection fields describing output dataset types.- Returns:
- struct
Struct Struct with attribute names corresponding to output connection fields
- struct
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: QuantumContext, inputRefs: InputQuantizedConnection, outputRefs: OutputQuantizedConnection) None¶
Do butler IO and transform to provide in memory objects for tasks
runmethod.- Parameters:
- butlerQC
QuantumContext 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
PipelineTaskConnectionsclass. The values of these attributes are thelsst.daf.butler.DatasetRefobjects associated with the defined input/prerequisite connections.- outputRefs
OutputQuantizedConnection Datastructure whose attribute names are the names that identify connections defined in corresponding
PipelineTaskConnectionsclass. The values of these attributes are thelsst.daf.butler.DatasetRefobjects associated with the defined output connections.
- butlerQC