ComputeExposureSummaryStatsTask¶

class lsst.pipe.tasks.computeExposureSummaryStats.ComputeExposureSummaryStatsTask(**kwargs)¶

Bases: Task

Task to compute exposure summary statistics.

This task computes various quantities suitable for DPDD and other downstream processing at the detector centers, including: - expTime - psfSigma - psfArea - psfIxx - psfIyy - psfIxy - ra - dec - pixelScale (arcsec/pixel) - 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 - psfApFluxDelta

This quantity is computed based on the aperture correction map, the psfSigma, and the image mask to assess the robustness of the aperture corrections across a given detector: - psfApCorrSigmaScaledDelta

These quantities are computed to assess depth: - effTime - effTimePsfSigmaScale - effTimeSkyBgScale - effTimeZeroPointScale - magLim

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_effective_time_stats`(summary, exposure)	Compute effective exposure time statistics to estimate depth.
`update_magnitude_limit_stats`(summary, exposure)	Compute a summary statistic for the point-source magnitude limit at a given signal-to-noise ratio using exposure-level metadata.
`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() → 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

lsst.utils.timer.logInfo: Implementation function.

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:

summarylsst.afw.image.ExposureSummaryStats: Summary object to update in-place.
backgroundlsst.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_effective_time_stats(summary, exposure)¶

Compute effective exposure time statistics to estimate depth.

The effective exposure time is the equivalent shutter open time that would be needed under nominal conditions to give the same signal-to-noise for a point source as what is achieved by the observation of interest. This metric combines measurements of the point-spread function, the sky brightness, and the transparency. It assumes that the observation is sky-background dominated.

The effective exposure time and its subcomponents are defined in [1].

Parameters:

summarylsst.afw.image.ExposureSummaryStats: Summary object to update in-place.
exposurelsst.afw.image.ExposureF: Exposure to grab band and exposure time metadata

References

[1]

Neilsen, E.H., Bernstein, G., Gruendl, R., and Kent, S. (2016). Limiting Magnitude, au, teff, and Image Quality in DES Year 1 https://www.osti.gov/biblio/1250877/

update_magnitude_limit_stats(summary, exposure)¶

Compute a summary statistic for the point-source magnitude limit at a given signal-to-noise ratio using exposure-level metadata.

The magnitude limit is calculated at a given SNR from the PSF, sky, zeropoint, and readnoise in accordance with SMTN-002 [1], LSE-40 [2], and DMTN-296 [3].

Parameters:

summarylsst.afw.image.ExposureSummaryStats: Summary object to update in-place.
exposurelsst.afw.image.ExposureF: Exposure to grab band and exposure time metadata

References

[1]

“Calculating LSST limiting magnitudes and SNR” (SMTN-002)

[2]

“Photon Rates and SNR Calculations” (LSE-40)

[3]

“Calculations of Image and Catalog Depth” (DMTN-296)

update_masked_image_stats(summary, masked_image)¶

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

Parameters:

summarylsst.afw.image.ExposureSummaryStats: Summary object to update in-place.
masked_imagelsst.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:

summarylsst.afw.image.ExposureSummaryStats: Summary object to update in-place.
photo_caliblsst.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, image_ap_corr_map=None, sources_is_astropy=False)¶

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

Parameters:

summarylsst.afw.image.ExposureSummaryStats: Summary object to update in-place.
psflsst.afw.detection.Psf or None: Point spread function model. If None, all fields that depend on the PSF will be reset (generally to NaN).
bboxlsst.geom.Box2I: Bounding box of the image for which summary stats are being computed.
sourceslsst.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_masklsst.afw.image.Mask, optional: Mask image that may be used to compute distance-to-nearest-star metrics.
sources_is_astropybool, 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:

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

Navigation

ComputeExposureSummaryStatsTask¶