ComputeExposureSummaryStatsTask¶

class lsst.pipe.tasks.computeExposureSummaryStats.ComputeExposureSummaryStatsTask(config: Optional[Config] = None, name: Optional[str] = None, parentTask: Optional[Task] = None, log: Optional[Union[logging.Logger, lsst.utils.logging.LsstLogAdapter]] = None)¶

Bases: lsst.pipe.base.Task

Task to compute exposure summary statistics.

This task computes various quantities suitable for DPDD and other downstream processing at the detector centers, including: - psfSigma - psfArea - psfIxx - psfIyy - psfIxy - ra - decl - zenithDistance - zeroPoint - skyBg - skyNoise - meanVar - raCorners - decCorners - astromOffsetMean - astromOffsetStd

These additional quantities are computed from the stars in the detector: - psfStarDeltaE1Median - psfStarDeltaE2Median - psfStarDeltaE1Scatter - psfStarDeltaE2Scatter - psfStarDeltaSizeMedian - psfStarDeltaSizeScatter - psfStarScaledDeltaSizeScatter

These quantities are computed based on the PSF model and image mask to assess the robustness of the PSF model across a given detector (against, e.g., extrapolation instability): - maxDistToNearestPsf - psfTraceRadiusDelta

Methods Summary

`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.
`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`(exposure, sources, background)	Measure exposure statistics from the exposure, sources, and background.
`timer`(name, logLevel)	Context manager to log performance data for an arbitrary block of code.
`update_background_stats`(summary, background)	Compute summary-statistic fields that depend only on the background model.
`update_masked_image_stats`(summary, masked_image)	Compute summary-statistic fields that depend on the masked image itself.
`update_photo_calib_stats`(summary, photo_calib)	Compute all summary-statistic fields that depend on the photometric calibration model.
`update_psf_stats`(summary, psf, bbox[, …])	Compute all summary-statistic fields that depend on the PSF model.
`update_wcs_stats`(summary, wcs, bbox, visitInfo)	Compute all summary-statistic fields that depend on the WCS model.

Methods Documentation

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

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.

run(exposure, sources, background)¶

Measure exposure statistics from the exposure, sources, and background.

Parameters:	exposure : `lsst.afw.image.ExposureF` sources : `lsst.afw.table.SourceCatalog` background : `lsst.afw.math.BackgroundList`
Returns:	summary : `lsst.afw.image.ExposureSummary`

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.

See also

timer.logInfo

Examples

Creating a timer context:

with self.timer("someCodeToTime"):
    pass  # code to time

update_background_stats(summary, background)¶

Compute summary-statistic fields that depend only on the background model.

Parameters:	summary : `lsst.afw.image.ExposureSummaryStats` Summary object to update in-place. background : `lsst.afw.math.BackgroundList` or `None` Background model. If `None`, all fields that depend on the background will be reset (generally to NaN).

Notes

This does not include fields that depend on the background-subtracted masked image; when the background changes, it should generally be applied to the image and update_masked_image_stats should be called as well.

update_masked_image_stats(summary, masked_image)¶

Compute summary-statistic fields that depend on the masked image itself.

Parameters:	summary : `lsst.afw.image.ExposureSummaryStats` Summary object to update in-place. masked_image : `lsst.afw.image.MaskedImage` or `None` Masked image. If `None`, all fields that depend on the masked image will be reset (generally to NaN).

update_photo_calib_stats(summary, photo_calib)¶

Compute all summary-statistic fields that depend on the photometric calibration model.

Parameters:	summary : `lsst.afw.image.ExposureSummaryStats` Summary object to update in-place. photo_calib : `lsst.afw.image.PhotoCalib` or `None` Photometric calibration model. If `None`, all fields that depend on the photometric calibration will be reset (generally to NaN).

update_psf_stats(summary, psf, bbox, sources=None, image_mask=None, sources_is_astropy=False)¶

Compute all summary-statistic fields that depend on the PSF model.

Parameters:

summary : lsst.afw.image.ExposureSummaryStats: Summary object to update in-place.
psf : lsst.afw.detection.Psf or None: Point spread function model. If None, all fields that depend on the PSF will be reset (generally to NaN).
bbox : lsst.geom.Box2I: Bounding box of the image for which summary stats are being computed.
sources : lsst.afw.table.SourceCatalog or astropy.table.Table: Catalog for quantities that are computed from source table columns. If None, these quantities will be reset (generally to NaN). The type of this table must correspond to the sources_is_astropy argument.
image_mask : lsst.afw.image.Mask, optional: Mask image that may be used to compute distance-to-nearest-star metrics.
sources_is_astropy : bool, optional: Whether sources is an astropy.table.Table instance instead of an lsst.afw.table.Catalog instance. Default is False (the latter).

update_wcs_stats(summary, wcs, bbox, visitInfo)¶

Compute all summary-statistic fields that depend on the WCS model.

Parameters:

summary : lsst.afw.image.ExposureSummaryStats: Summary object to update in-place.
wcs : lsst.afw.geom.SkyWcs or None: Astrometric calibration model. If None, all fields that depend on the WCS will be reset (generally to NaN).
bbox : lsst.geom.Box2I: Bounding box of the image for which summary stats are being computed.
visitInfo : lsst.afw.image.VisitInfo: Observation information used in together with wcs to compute the zenith distance.

Navigation

ComputeExposureSummaryStatsTask¶