MakeDirectWarpTask¶

class lsst.pipe.tasks.make_direct_warp.MakeDirectWarpTask(**kwargs)¶

Bases: PipelineTask

Warp single-detector images onto a common projection.

This task iterates over multiple images (corresponding to different detectors) from a single visit that overlap the target patch. Pixels that receive no input from any detector are set to NaN in the output image, and NO_DATA bit is set in the mask plane.

This differs from the standard MakeWarp Task in the following ways:

No selection on ccds at the time of warping. This is done later during the coaddition stage.
Interpolate over a set of masked pixels before warping.
Generate an image where each pixel denotes how much of the pixel is masked.
Generate multiple noise warps with the same interpolation applied.
No option to produce a PSF-matched warp.

Attributes Summary

canMultiprocess

Methods Summary

`compute_median_variance`(mi)	Compute the median variance across the good pixels of a MaskedImage.
`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.
`getTaskDict`()	Get a dictionary of all tasks as a shallow copy.
`get_seed_from_data_id`(data_id)	Get a seed value given a data_id.
`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.
`make_noise_exposures`(calexp, rng)	Make pure noise realizations based on `calexp`.
`process`(detector_inputs, target_wcs, warper)	Process an exposure.
`run`(inputs, sky_info, visit_summary)	Create a Warp dataset from inputs.
`runQuantum`(butlerQC, inputRefs, outputRefs)	Do butler IO and transform to provide in memory objects for tasks `run` method.
`timer`(name[, logLevel])	Context manager to log performance data for an arbitrary block of code.

Attributes Documentation

canMultiprocess: ClassVar[bool] = True¶

Methods Documentation

static compute_median_variance(mi: MaskedImage) → float¶

Compute the median variance across the good pixels of a MaskedImage.

Parameters:

miMaskedImage: The input image on which to compute the median variance.

Returns:

median_variancefloat: Median variance of the input calexp.

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

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: Get the full name of the task.

getTaskDict() → dict[str, weakref.ReferenceType[lsst.pipe.base.task.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.

get_seed_from_data_id(data_id) → int¶

Get a seed value given a data_id.

This method generates a unique, reproducible pseudo-random number for a data id. This is not affected by ordering of the input, or what set of visits, ccds etc. are given.

This is implemented as a public method, so that simulations that don’t necessary deal with the middleware can mock up a data_id instance, or override this method with a different one to obtain a seed value consistent with the pipeline task.

Parameters:

data_idDataCoordinate: Data identifier dictionary.

Returns:

seedint: A unique seed for this data_id to seed a random number generator.

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.

make_noise_exposures(calexp: ExposureF, rng) → dict[int, lsst.afw.image._exposure.ExposureF]¶

Make pure noise realizations based on calexp.

Parameters:

calexpExposureF: The input exposure on which to base the noise realizations.
rngnp.random.RandomState: Random number generator to use for the noise realizations.

Returns:

noise_calexpsdict [int, ExposureF]: A mapping of integers ranging from 0 up to config.numberOfNoiseRealizations to the corresponding noise realization exposures.

process(detector_inputs: WarpDetectorInputs, target_wcs, warper, visit_summary=None, maxBBox=None, destBBox=None) → ExposureF | None¶

Process an exposure.

There are three processing steps that are applied to the input:

Interpolate over bad pixels before warping.

Apply all calibrations from visit_summary to the exposure.

Warp the exposure to the target coordinate system.

Parameters:

detector_inputsWarpDetectorInputs: The input exposure to be processed, along with any other per-detector modifications.
target_wcsSkyWcs: The WCS of the target patch.
warperWarper: The warper to use for warping the input exposure.
visit_summaryExposureCatalog | None: Table of visit summary information. If not None, the visit_summary information will be used to update the calibration of the input exposures. Otherwise, the input exposures will be used as-is.
maxBBoxBox2I | None: Maximum bounding box of the warped exposure. If None, this is determined automatically.
destBBoxBox2I | None: Exact bounding box of the warped exposure. If None, this is determined automatically.

Returns:

warped_exposureExposure | None: The processed and warped exposure, if all the calibrations could be applied successfully. Otherwise, None.

run(inputs: Mapping[int, WarpDetectorInputs], sky_info, visit_summary) → Struct¶

Create a Warp dataset from inputs.

Parameters:

inputsMapping [ int, WarpDetectorInputs ]: Dictionary of input datasets, with the detector id being the key.
sky_infoStruct: A Struct object containing wcs, bounding box, and other information about the patches within the tract.
visit_summaryExposureCatalog | None: Table of visit summary information. If provided, the visit summary information will be used to update the calibration of the input exposures. If None, the input exposures will be used as-is.

Returns:

resultsStruct: A Struct object containing the warped exposure, noise exposure(s), and masked fraction image.

runQuantum(butlerQC, inputRefs, outputRefs)¶

Do butler IO and transform to provide in memory objects for tasks run method.

Parameters:

butlerQCQuantumContext: 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.

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.
logLevelint: A logging level constant.

Navigation

MakeDirectWarpTask¶