IsrStatisticsTask¶

class lsst.ip.isr.IsrStatisticsTask(statControl=None, **kwargs)¶

Bases: Task

Task to measure arbitrary statistics on ISR processed exposures.

The goal is to wrap a number of optional measurements that are useful for calibration production and detector stability.

Methods Summary

`copyCalibDistributionStatistics`(inputExp, ...)	Copy calibration statistics for this exposure.
`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.
`makeKernel`(kernelSize)	Make a boxcar smoothing kernel.
`makeSubtask`(name, **keyArgs)	Create a subtask as a new instance as the `name` attribute of this task.
`measureAmpCorrelations`(inputExp, ...)	Measure correlations between amplifier segments.
`measureBanding`(inputExp, overscans)	Task to measure banding statistics.
`measureBiasShifts`(inputExp, ...)	Measure number of bias shifts from overscan data.
`measureCti`(inputExp, untrimmedInputExp, gains)	Task to measure CTI statistics.
`measureCtiLegacy`(inputExp, serialOverscans, ...)	Task to measure CTI statistics.
`measureDivisaderoStatistics`(inputExp, **kwargs)	Measure Max Divisadero Tearing effect per amp.
`measureProjectionStatistics`(inputExp, overscans)	Task to measure metrics from image slicing.
`run`(inputExp[, untrimmedInputExposure, ptc, ...])	Task to run arbitrary statistics.
`timer`(name[, logLevel])	Context manager to log performance data for an arbitrary block of code.

Methods Documentation

copyCalibDistributionStatistics(inputExp, **kwargs)¶

Copy calibration statistics for this exposure.

Parameters:

inputExplsst.afw.image.Exposure: The exposure being processed.
**kwargs: Keyword arguments with calibrations.

Returns:

outputStatsdict [str, [dict [str, float]]]: Dictionary of measurements, keyed by amplifier name and statistics segment.

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.

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")

static makeKernel(kernelSize)¶

Make a boxcar smoothing kernel.

Parameters:

kernelSizeint: Size of the kernel in pixels.

Returns:

kernelnp.array: Kernel for boxcar smoothing.

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.

measureAmpCorrelations(inputExp, serialOverscanResults)¶

Measure correlations between amplifier segments.

Parameters:

inputExplsst.afw.image.Exposure

Exposure to measure.

overscanslist [lsst.pipe.base.Struct]

List of overscan results. Expected fields are:

imageFit: Value or fit subtracted from the amplifier image data (scalar or lsst.afw.image.Image).
overscanFit: Value or fit subtracted from the overscan image data (scalar or lsst.afw.image.Image).
overscanImage: Image of the overscan region with the overscan correction applied (lsst.afw.image.Image). This quantity is used to estimate the amplifier read noise empirically.

Returns:

outputStatsdict [str, [dict [str, float]]]: Dictionary of measurements, keyed by amplifier name and statistics segment.

Notes

Based on eo_pipe implementation: https://github.com/lsst-camera-dh/eo_pipe/blob/main/python/lsst/eo/pipe/raft_level_correlations.py # noqa: E501 W505

measureBanding(inputExp, overscans)¶

Task to measure banding statistics.

Parameters:

inputExplsst.afw.image.Exposure

Exposure to measure.

overscanslist [lsst.pipe.base.Struct]

List of overscan results. Expected fields are:

imageFit: Value or fit subtracted from the amplifier image data (scalar or lsst.afw.image.Image).
overscanFit: Value or fit subtracted from the overscan image data (scalar or lsst.afw.image.Image).
overscanImage: Image of the overscan region with the overscan correction applied (lsst.afw.image.Image). This quantity is used to estimate the amplifier read noise empirically.

Returns:

outputStatsdict [str, [dict [str, float]]]: Dictionary of measurements, keyed by amplifier name and statistics segment.

measureBiasShifts(inputExp, serialOverscanResults)¶

Measure number of bias shifts from overscan data.

Parameters:

inputExplsst.afw.image.Exposure

Exposure to measure.

overscanslist [lsst.pipe.base.Struct]

List of overscan results. Expected fields are:

imageFit: Value or fit subtracted from the amplifier image data (scalar or lsst.afw.image.Image).
overscanFit: Value or fit subtracted from the overscan image data (scalar or lsst.afw.image.Image).
overscanImage: Image of the overscan region with the overscan correction applied (lsst.afw.image.Image). This quantity is used to estimate the amplifier read noise empirically.

Returns:

outputStatsdict [str, [dict [str, float]]]: Dictionary of measurements, keyed by amplifier name and statistics segment.

Notes

Based on eo_pipe implementation: https://github.com/lsst-camera-dh/eo_pipe/blob/main/python/lsst/eo/pipe/biasShiftsTask.py # noqa: E501 W505

measureCti(inputExp, untrimmedInputExp, gains)¶

Task to measure CTI statistics.

Parameters:

inputExplsst.afw.image.Exposure: The exposure to measure.
untrimmedInputExplsst.afw.image.Exposure: Exposure to measure overscan from.
gainsdict [str float]: Dictionary of per-amplifier gains, indexed by amplifier name.

Returns:

outputStatsdict [str, [dict [str, float]]]: Dictionary of measurements, keyed by amplifier name and statistics segment. Everything in units based on electron.

Notes

The input exposure is needed because it contains the last imaging pixel, with defects applied. And the untrimmed input exposure is needed because it contains the overscan regions. It needs to be this way because the defect masking code requires that the image be trimmed, but we need the image with defects masked to measure the CTI from the last imaging pixel.

measureCtiLegacy(inputExp, serialOverscans, gains)¶

Task to measure CTI statistics.

Parameters:

inputExplsst.afw.image.Exposure

Exposure to measure.

serialOverscanslist [lsst.pipe.base.Struct]

List of serial overscan results (expects base units of adu). Expected fields are:

imageFit: Value or fit subtracted from the amplifier image data (scalar or lsst.afw.image.Image).
overscanFit: Value or fit subtracted from the overscan image data (scalar or lsst.afw.image.Image).
overscanImage: Image of the overscan region with the overscan correction applied (lsst.afw.image.Image). This quantity is used to estimate the amplifier read noise empirically.

gainsdict [str float]

Dictionary of per-amplifier gains, indexed by amplifier name.

Returns:

outputStatsdict [str, [dict [str, float]]]: Dictionary of measurements, keyed by amplifier name and statistics segment. Everything in units based on electron.

measureDivisaderoStatistics(inputExp, **kwargs)¶

Measure Max Divisadero Tearing effect per amp.

Parameters:

inputExplsst.afw.image.Exposure: Exposure to measure. Usually a flat.
**kwargs: The flat will be selected from here.

Returns:

outputStatsdict [str, [dict [str, float]]]

Dictionary of measurements, keyed by amplifier name and statistics segment. Measurements include

DIVISADERO_PROFILE: Robust mean of rows between divisaderoProjection<Maximum|Minumum> on readout edge of ccd normalized by a linear fit to the same rows.
DIVISADERO_MAX_PAIR: Tuple of maximum of the absolute values of the DIVISADERO_PROFILE, for number of pixels (specified by divisaderoNumImpactPixels on left and right side of amp.
DIVISADERO_MAX: Maximum of the absolute values of the the DIVISADERO_PROFILE, for the divisaderoNumImpactPixels on boundaries of neighboring amps (including the pixels in those neighborboring amps).

measureProjectionStatistics(inputExp, overscans)¶

Task to measure metrics from image slicing.

Parameters:

inputExplsst.afw.image.Exposure

Exposure to measure.

overscanslist [lsst.pipe.base.Struct]

List of overscan results. Expected fields are:

imageFit: Value or fit subtracted from the amplifier image data (scalar or lsst.afw.image.Image).
overscanFit: Value or fit subtracted from the overscan image data (scalar or lsst.afw.image.Image).
overscanImage: Image of the overscan region with the overscan correction applied (lsst.afw.image.Image). This quantity is used to estimate the amplifier read noise empirically.

Returns:

outputStatsdict [str, [dict [str, float]]]: Dictionary of measurements, keyed by amplifier name and statistics segment.

run(inputExp, untrimmedInputExposure=None, ptc=None, serialOverscanResults=None, parallelOverscanResults=None, doLegacyCtiStatistics=False, **kwargs)¶

Task to run arbitrary statistics.

The statistics should be measured by individual methods, and add to the dictionary in the return struct.

Parameters:

inputExplsst.afw.image.Exposure

The exposure to measure.

untrimmedInputExp

The exposure to measure overscan statistics from.

ptclsst.ip.isr.PtcDataset, optional

A PTC object containing gains to use.

serialOverscanResultslist [lsst.pipe.base.Struct], optional

List of serial overscan results. Expected fields are:

imageFit: Value or fit subtracted from the amplifier image data (scalar or lsst.afw.image.Image).
overscanFit: Value or fit subtracted from the overscan image data (scalar or lsst.afw.image.Image).
overscanImage: Image of the overscan region with the overscan correction applied (lsst.afw.image.Image). This quantity is used to estimate the amplifier read noise empirically.

parallelOverscanResultslist [lsst.pipe.base.Struct], optional

List of parallel overscan results. Expected fields are:

imageFit: Value or fit subtracted from the amplifier image data (scalar or lsst.afw.image.Image).
overscanFit: Value or fit subtracted from the overscan image data (scalar or lsst.afw.image.Image).
overscanImage: Image of the overscan region with the overscan correction applied (lsst.afw.image.Image). This quantity is used to estimate the amplifier read noise empirically.

doLegacyCtiStatisticsbool, optional

Use the older version of measureCti (not recommended). This should be True if and only if this task is called from IsrTask. TODO: Deprecate legacy CTI + CTI correction from IsrTask (DM-48757).

**kwargs

Keyword arguments. Calibrations being passed in should have an entry here.

Returns:

resultStructlsst.pipe.base.Struct: Contains the measured statistics as a dict stored in a field named results.

Raises:

RuntimeError: Raised if the amplifier gains could not be found.

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

IsrStatisticsTask¶